Making great software, great product that stands the test of time and not just survives but thrives through monumental technological shifts is incredibly hard. That challenge is part of the reason I love doing it. There is never a dull day, and the reward of seeing the code you wrote used by the most amazing creators in the world is an indescribable pleasure. When I see what people create with WordPress, some days I feel like I’m grinding pigment for Leonardo da Vinci or slitting a quill for Beethoven.
In open source, one thing that makes it even harder to ship great software is bringing together disparate groups of contributors who may have entirely different incentives or missions or philosophies about how to make great work. Working together on a team is such a delicate balance, and even one person rowing in the wrong direction can throw everyone else off.
That’s why periodically I think it is very healthy for open source projects to fork, it allows for people to try out and experiment with different forms of governance, leadership, decision-making, and technical approaches. As I’ve said, forking is beautiful, and forks have my full support and we’ll even link and promote them.
Karim leads a small WordPress agency called Crowd Favorite which counts clients such as Lexus and ABC and employs ~50 people.
Both are men I have shared meals with and consider of the highest integrity. I would trust them to watch any of my 15 godchildren for a day. These are good humans. Now go do the work. It probably won’t happen on day one, but Joost and Karim’s fork, which I’ll call JKPress until they come up with a better name, has a number of ideas they want to try out around governance and architecture. While Joost and Karim will be unilaterally in charge in the beginning, it sounds like they want to set up:
A non-profit foundation, with a broad board to control their new project.
A website owned by that foundation which hosts community resources like a plugin directory, forums, etc.
No more centralized and moderated plugin and theme directories with security guidelines or restrictions are what plugins are allowed to do like putting banners in your admin or gathering data, everything done in a federated/distributed manner.
The trademarks for their new project will either be public domain or held by their foundation.
“Modernization” of the technology stack, perhaps going a Laravel-like approach or changing how WordPress’ architecture works.
Teams and committees to make decisions for everything, so no single person has too much power or authority.
I'm ready to lead the next releases. I am sure plenty of people and companies are willing to help me and we've got plenty of ideas on what we should be doing.#WordPress
Now, as core committer Jb Audras (not employed by me or Automattic) points out, within WordPress we have a process in which people earn the right to lead a release:
Before leading any major release of WordPress, please start with leading a minor one @jdevalk. Then, apply to be Triage Lead or Coordination Lead Deputy for a major release. These are the steps everyone in our community should follow before claiming to run « the next releases ».
However in Joost and Karim’s new project, they don’t need to follow our process or put in the hours to prove their worth within the WordPress.org ecosystem, they can just lead by example by shipping code and product to people that they can use, evaluate, and test out for themselves. If they need financial or hosting support is sounds like WP Engine wants to support their fork:
We appreciate @jdevalk and @karimmarucchi thoughtful call for constructive conversation, change and evolved leadership within the WordPress community. Moments of disruption challenge all of us to reflect and to act.
WordPress’s success as the most widely used CMS is not the…
Awesome! (Maybe it’s so successful they rebrand as JK Engine in the future.) WP Engine, with its half a billion in revenue and 1,000+ employees, has more than enough resources to support and maintain a legitimate fork of WordPress. And they are welcome to use all the GPL code myself and others have created to do so, including many parts of WordPress.org that are open source released under the GPL, and Gutenberg which is GPL + MPL.
Joost also is a major investor (owner?) in Post Status (which he tried to sell to me a few months ago, and I declined to buy, perhaps kicking off his consternation with me), so they have a news media site and Slack instance already ready to go. He also is an investor in PatchStack and appears to be trying to create a new business around something called Progress Planner, both of which could be incorporated into the new non-profit project to give them some competitive distinctions from WordPress.
To make this easy and hopefully give this project the push it needs to get off the ground, I’m deactivating the .org accounts of Joost, Karim, Se Reed, Heather Burns, and Morten Rand-Hendriksen. I strongly encourage anyone who wants to try different leadership models or align with WP Engine to join up with their new effort.
In the meantime, on top of my day job running a 1,700+ person company with 25+ products, which I typically work 60-80 hours a week on, I’ll find time on nights and weekends to work on WordPress 6.8 and beyond. Myself and other “non-sponsored” contributors have been doing this a long time and while we may need to reduce scope a bit I think we can put out a solid release in March.
Joost and Karim have a number of bold and interesting ideas, and I’m genuinely curious to see how they work out. The beauty of open source is they can take all of the GPL code in WordPress and ship their vision. You don’t need permission, you can just do things. If they create something that’s awesome, we may even merge it back into WordPress, that ability for code and ideas to freely flow between projects is part of what makes open source such an engine for innovation. I propose that in a year we do a WordPress + JKPress summit, look at what we’ve shipped and learned in the process, which I’d be happy to host and sponsor in NYC next January 2026. The broader community will benefit greatly from this effort, as it’s giving us a true chance to try something different and see how it goes.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18394";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:1;a:6:{s:4:"data";s:57:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:67:"WordPress Themes Need More Weird: A Call for Creative Digital Homes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:102:"https://wordpress.org/news/2025/01/wordpress-themes-need-more-weird-a-call-for-creative-digital-homes/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Thu, 02 Jan 2025 18:53:06 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:6:"Design";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18358";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:409:"The modern web has gradually shifted from a vibrant tapestry of personal expression to a landscape of identical designs, where millions of websites share not just similar structures, but identical visual language, spacing, and interaction patterns. As we collectively gravitate toward the same “proven” layouts and “conversion-optimized” designs, we’re not just losing visual diversity – […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Nick Hamze";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:7631:"
The modern web has gradually shifted from a vibrant tapestry of personal expression to a landscape of identical designs, where millions of websites share not just similar structures, but identical visual language, spacing, and interaction patterns. As we collectively gravitate toward the same “proven” layouts and “conversion-optimized” designs, we’re not just losing visual diversity – we’re ceding control over how we present ourselves to the world. This matters because genuine self-expression online isn’t just about aesthetics – it’s about maintaining spaces where authentic voices can flourish.
When every blog has the same hero section, when every portfolio follows the same grid, when every restaurant site looks interchangeable, we create an echo chamber of sameness. The cost isn’t just visual monotony – it’s the slow erosion of the web’s ability to surprise, delight, and showcase truly individual perspectives. WordPress, with its emphasis on complete ownership and control, offers an opportunity to break free from this convergence of design, allowing creators to build digital spaces that truly reflect their unique voice and vision.
Think of WordPress themes like album covers. They should have personality and create an immediate visual impact. The web has become too sanitized, with everyone chasing the same minimal, “professional” look.
Great themes should:
Have a strong point of view – like how Kubrick (the classic WordPress theme) defined an era with its distinctive header gradient. Don’t try to be everything to everyone.
Embrace specific aesthetics boldly – whether that’s brutalist design, pixel art, hand-drawn elements, or distinctive typography. Create themes that excite people rather than just working for everyone.
Design for specific use cases – like a theme for photographers that’s all about full-bleed images or a theme for writers that treats typography as art or a theme for musicians that feels like an album cover.
Break some rules thoughtfully – because not every theme needs a hamburger menu. Not every theme needs to be mobile-first. Sometimes constraints create character.
We need more themes that make people say “Wow!” or “That’s different!” rather than “That’s clean and professional.” The web needs more personality, more risk-taking, more fun.
After spending countless hours digging through the WordPress theme repository, searching for designs that break the mold and spark excitement, I came up nearly empty-handed. Don’t get me wrong – there are plenty of well-built themes out there. But where’s the daring? The personality? The unexpected?
If you’ve got a wild theme idea burning in your mind – that portfolio theme that looks like a vintage trading card collection, that blog theme inspired by zine culture, that restaurant theme that feels like a hand-drawn menu – now’s the time to build it. WordPress desperately needs your creativity, your weird ideas, your willingness to break the visual rules. The future of the web shouldn’t be a monochrome landscape of identical layouts. Let’s make WordPress themes exciting again. Let’s make the web weird again.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18358";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:2;a:6:{s:4:"data";s:57:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:13:"Holiday Break";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:49:"https://wordpress.org/news/2024/12/holiday-break/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Fri, 20 Dec 2024 00:36:59 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18328";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:362:"In order to give myself and the many tired volunteers around WordPress.org a break for the holidays, we’re going to be pausing a few of the free services currently offered: We’re going to leave things like localization and the forums open because these don’t require much moderation. As you may have heard, I’m legally compelled […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Matt Mullenweg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2315:"
In order to give myself and the many tired volunteers around WordPress.org a break for the holidays, we’re going to be pausing a few of the free services currently offered:
New account registrations on WordPress.org (clarifying so press doesn’t confuse this: people can still make their own WordPress installs and accounts)
I hope to find the time, energy, and money to reopen all of this sometime in the new year. Right now much of the time I would spend making WordPress better is being taken up defending against WP Engine’s legal attacks. Their attacks are against Automattic, but also me individually as the owner of WordPress.org, which means if they win I can be personally liable for millions of dollars of damages.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18328";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:3;a:6:{s:4:"data";s:60:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:57:"State of the Word 2024: Legacy, Innovation, and Community";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:90:"https://wordpress.org/news/2024/12/state-of-the-word-2024-legacy-innovation-and-community/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Mon, 16 Dec 2024 21:28:22 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:2:{i:0;a:5:{s:4:"data";s:6:"Events";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"state of the word";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18205";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:282:"On a memorable evening in Tokyo, State of the Word 2024 brought together WordPress enthusiasts from around the world—hundreds in person and millions more online. This event marked the first time State of the Word was hosted in Asia, reflecting the platform\'s growing global reach.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:17:"Nicholas Garofalo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:88600:"
On a memorable evening in Tokyo, State of the Word 2024 brought together WordPress enthusiasts from around the world—hundreds in person and millions more online. This event marked the first time State of the Word was hosted in Asia, reflecting the platform’s growing global reach. The setting couldn’t have been more fitting: a city where tradition and technology coexist in seamless harmony. Tokyo, much like WordPress itself, reflects a powerful blend of legacy and innovation, craftsmanship and technology, and moments of vast scale balanced by serene stillness.
Tokyo is a city you feel.
Matt Mullenweg, WordPress Cofounder
During the event, the concept of kansei engineering emerged as a central theme. This Japanese design philosophy seeks to create experiences that go beyond function and aesthetics, focusing on how something feels. As highlighted during the keynote, this principle has quietly influenced WordPress’s development, shaping its design and user experience in ways that resonate on an instinctive level.
The evening also celebrated Japan’s deep-rooted connection to WordPress. Nearly 21 years ago, Japan became the first country to localize WordPress, long before a formal translation framework existed. It all started with a single forum post from a user named Otsukare, launching a translation project that helped WordPress become a truly global platform. Seeing how far the Japanese WordPress community has come—both in market share and cultural influence—was a powerful reminder of what shared purpose can achieve.
Wapuu, WordPress’s beloved mascot, was also born in Japan. What began as a simple idea for a fun and friendly representation of WordPress evolved into a global phenomenon. Thanks to Kazuko Kaneuchi’s generous open-source contribution, Wapuu has been reimagined by WordPress communities worldwide, each version infused with local character. This uniquely Japanese creation has helped make WordPress more welcoming, approachable, and fun wherever it appears.
WordPress Growth in 2024
WordPress cofounder Matt Mullenweg highlighted significant achievements that underscored WordPress’s growth, resilience, and expanding global presence in 2024. He shared that WordPress now powers 43.6% of all websites globally. In Japan, WordPress’s influence is even more pronounced, powering 58.5% of all websites. This remarkable statistic reinforces the platform’s enduring role as a cornerstone of the open web and accentuates Japan’s deep-rooted commitment to the WordPress ecosystem and its developers’ significant contributions.
WordPress sites using languages other than English are expected to surpass English-language sites by 2025. German recently overtook Japanese as the third-most-used language, though Japanese remained close behind. Meanwhile, emerging languages like Farsi experienced rapid adoption, reflecting the platform’s expanding multilingual ecosystem. In Southeast Asia, languages such as Indonesian, Vietnamese, and Thai saw substantial year-over-year growth, signaling broader adoption across diverse regions.
Core downloads surged to nearly half a billion annually, with the notable releases of WordPress 6.5, 6.6, and 6.7.
WordPress’s design and development ecosystem flourished as well. Over 1,700 new themes were uploaded in 2024, bringing more than 1,000 block themes to the official repository and reflecting increased interest in modern, flexible site design.
The plugin ecosystem also saw record-breaking activity this year. Plugin downloads surged toward 2.35 billion, representing a 20% year-over-year increase. Plugin updates exceeded 3 billion and are on track to surpass 3.5 billion by year’s end. Notably, the Plugin Review Team made transformative improvements, drastically reducing the average review wait time. Their efficiency gains were complemented by the launch of the Plugin Check tool, which reduced submission issues by 41% while enabling the team to approve 138% more plugins each week.
These accomplishments showcase WordPress’s resilience, adaptability, and ever-expanding influence. As the platform continues to evolve, its global community remains at the heart of its success, driving innovation and ensuring that WordPress thrives as the leading tool for building the open web.
Help shape the future of WordPress: Join a contributor team today!
WordPress lead architect, Matías Ventura, highlighted WordPress’s evolution through the lenses of writing, design, building, and development, demoing various pieces of new and forthcoming enhancements.
Writing
The writing experience in WordPress saw notable advancements this year, with an improved distraction-free mode that helps users to focus on content creation without interface distractions. Now you can directly select the image itself to drag and drop it where you want, even enabling on-the-fly gallery creation when you drop images next to each other.
Additionally, the introduction of block-level comments in the editor, currently an experimental feature, promises to reshape collaborative workflows by enabling teams to leave notes directly on blocks.
These enhancements all work together to make writing, composing, and editing in WordPress feel more fluid, personal, and pleasant than ever.
Design
Along with new default theme Twenty Twenty-Five, more than 1,000 block themes offer tailored starting points for different site types, including portfolios, blogs, and business sites. Designers can also utilize the improved Style Book for a comprehensive view of their site’s appearance, ensuring a smooth design process.
Design work isn’t just about aesthetics—it’s also about creating the right environment and guardrails. It’s important that users can interact with their site, add content, replace media, and choose sections without needing to know the layout details. We’re implementing better default experiences to help you focus exclusively on the content or on the design, depending on your needs at the moment.
This all works seamlessly with the zoom-out view, where users can compose content using patterns without having to set up every individual block. Having a bird’s-eye view of your site can really help you gain a different perspective.
These design capabilities scale with you as your WordPress projects grow. WordPress’s approach to design is systematic: blocks combine to form patterns, patterns form templates, and templates help separate content from presentation.
Building
WordPress’s content management capabilities allow working at scale and across teams. Central to this is the introduction of Block Bindings, which merge the flexibility of blocks with the structured power of meta fields. This feature allows block attributes to be directly linked to data sources like post meta, reducing the need for custom blocks while creating deeper, more dynamic content relationships. The familiar block interface remains intact, making complex data management feel seamless. This connects naturally with our broader work on Data Views for post types and meta fields.
These updates reinforce WordPress’s role as a powerful content management system by connecting its core primitives—blocks, post types, taxonomies, and meta fields—more intuitively.
Development
Lastly, Matías showcased a range of groundbreaking tools that empower WordPress developers and streamline their workflows. One of the highlights was the new Templates API, which has simplified the process of registering and managing custom templates. Future updates to the API will allow users to register and activate templates seamlessly, enabling dynamic site customizations such as scheduling different homepage templates for special events or swapping category archives during campaigns. This flexible approach offers developers greater creative control in a standardized way.
The session also explored the Interactivity API, designed to deliver fast, seamless website experiences by enabling server-rendered interactivity within WordPress. Unlike JavaScript-heavy frameworks, this technology keeps everything within WordPress’s existing ecosystem, bridging the gap between developers and content creators. Attendees saw live demos showcasing instant search, pagination, and commenting—all without page reloads—while maintaining a perfect performance score of 100 on Lighthouse. In addition, it was announced that responsive controls will receive significant attention, with new features being explored, like block visibility by breakpoint and adding min/max controls to the columns block.
The WordPress Playground also emerged as a game-changer, allowing users to spin up WordPress sites directly in their browsers, experiment with Blueprints, and manage projects offline. With improved GitHub integration and expanded documentation, WordPress developers now have a more accessible and powerful toolkit than ever before.
An AI Future
Returning to the stage, Matt noted that Gutenberg’s evolution is paving the way for AI-powered site building while keeping creative control in users’ hands. A recent speed building challenge on WordPress’s YouTube channel showcased this potential, with Nick Diego using AI-assisted tools and Ryan Welcher building manually. While the AI-assisted approach won, the key takeaway was that AI isn’t here to replace developers but to enhance creativity and efficiency.
Community Impact and Global Reach
When WordPress Executive Director Mary Hubbard took the stage, she emphasized WordPress’s commitment to its open-source mission and the power of its global community. Mary shared her passion for defending WordPress’s principles, reaffirming that when users choose WordPress, they should receive the authentic, community-driven experience that the platform stands for. This commitment to clarity, trust, and open-source integrity is central to ensuring WordPress’s long-term sustainability and success.
Mary Hubbard, WordPress Executive Director
In 2024, WordPress’s global influence surged through expanded educational programs, developer contributions, and grassroots initiatives. The platform’s social media following grew to 2.3 million, while major events like WordCamps and live-streamed gatherings attracted millions of attendees and viewers, connecting people worldwide.
Learn WordPress introduced Structured Learning Pathways, offering tailored tracks for beginners and developers, fostering a growing network of creators eager to learn and contribute. Grassroots programs flourished, with WP Campus Connect bringing WordPress education to Indian colleges and innovation competitions in Uganda empowering young creators. In Latin America, the Community Reactivation Project reignited meetups across nine cities, fostering a network of over 150 active members and setting the stage for three new WordCamps in 2025.
WordPress’s efforts also advanced through Openverse, which expanded its free content library to 884 million images and 4.2 million audio files, serving millions of creators worldwide and supporting WordPress’s broader mission of democratizing publishing.
Whether through educational platforms, developer-driven innovation, or community-led projects, WordPress’s ecosystem continues to nurture shared learning, creativity, and collaboration, ensuring its growth and relevance for future generations.
Japanese Community Highlights
Junko Fukui Nukaga—Community Team rep, program manager, and WordCamp organizer—noted that WordPress’s prominence in Japan contributes to an economy now estimated to exceed 100 billion yen.
In October of 2024, the Japanese WordPress community celebrated DigitalCube’s IPO on the Tokyo PRO Market, marking a milestone for the local WordPress ecosystem. Major contributors like Takayuki Miyoshi’s Contact Form 7 plugin surpassed 10 million active users, while companies like Sakura Internet and XServer built specialized WordPress infrastructure.
Community events in Japan have also flourished, with 189 local meetups held throughout the year, fueled by dedicated volunteers and organizers. Translation Night gatherings have ensured WordPress remains accessible to Japanese users, reflecting a thriving collaborative spirit.
Matt gave special recognition to Japan’s standout contributor, Aki Hamano, a Core Committer whose exceptional efforts elevated WordPress development over the past year. Hamano-san made an impressive 774 contributions to WordPress core, earning 162 props for WordPress 6.5, rising to 274 props for 6.6 as the second-highest contributor, and securing the top spot with 338 props for 6.7.Other notable Japanese contributors included Akira Tachibana, an active Docs Team member, and Nukaga, recognized for her exceptional community organizing efforts. Additionally, 13 Japanese contributors supported 5.4% of WordPress 6.6 development, showcasing the country’s growing influence in the WordPress ecosystem.
Data Liberation
Reflecting on the progress since the initiative’s launch last year, the focus remained on ensuring that WordPress not only becomes more powerful but also embodies freedom in its deepest sense—the freedom to move content anywhere, collaborate without limits, and create without constraints. This vision extends beyond individual sites to a broader web where content flows seamlessly across platforms, enabling unrestricted creativity and innovation.
One compelling example demonstrated how easily ePub files could be imported into a WordPress site, integrating seamlessly with existing designs. This represents the initiative’s broader goal: making content migration and integration effortless. WordPress Playground plays a critical role in this vision by enabling easy site migration through a simple browser extension. With Playground as a staging area, migrating and adapting sites becomes intuitive and accessible.
Q&A
The floor was opened to questions in both Japanese and English.
Questions from the audience, including Tokyo Vice author Jake Adelstein, covered the future of blogging, WordPress performance, the impact of AI search, and what democratizing publishing means today. Matt shared his excitement for more open platforms such as Mastodon and Bluesky, as well as his recommendations for optimizing your site for both humans and AI. A common thread throughout was that a personal website is an important part of your digital identity, and WordPress allows you to express yourself in fun and unique ways.
Panels
After attendees enjoyed a special performance by the pianist, Takai-san, industry leaders, creators, and innovators took the stage for panel discussions about the present and future of WordPress, moderated by Mary Hubbard.
Publishing in the Open
Featuring:
Mieko Kawakami, Japanese Author and Poet
Craig Mod, Author of Things Become Other Things
Matt Mullenweg, WordPress Cofounder and Automattic CEO
This first panel explored the transformative power of open-source publishing. Panelists shared insights into how open publishing has influenced their creative journeys, expanded audience engagement, and shaped storytelling across cultural boundaries.
Publishing in the open has defined what I’ve done. All the best connections I’ve made in live have been the result of publishing in the open. – Craig Mod
Publishing in the open, like WordPress, is about building community, mutual connections, and putting power back into the hands of creators.
The Future of WordPress in Japan and Beyond
Featuring:
Hajime Ogushi, mgn CEO
Genki Taniguchi, SAKURA internet Inc. Senior Director
Matt Mullenweg, WordPress Cofounder and Automattic CEO
The second discussion highlighted WordPress’s remarkable growth in Japan and its broader global impact. The discussion covered the drivers behind Japan’s adoption of WordPress, its thriving ecosystem of WordPress-based businesses, and emerging trends in web development.
Compared to other CMSs the WordPress Japanese is much easier to use. – Hajime Ogushi
The group discussed plugins such as Contact Form 7, the affordability of hosting WordPress, and local meetups and events
Closing
Thank you to all the guests who joined us on stage, those who ventured to Tokyo, and everyone who tuned in from around the world. Today’s event showcased how a free and infinitely flexible platform, an active global community, open innovation, and a commitment to a fully democratized web make us better at being who we are.
From Tokyo, Arigatou Gozaimashita!
For those interested in exploring past State of the Word keynotes, WordPress has curated a comprehensive YouTube playlist featuring keynotes from previous years. Watch them all here: State of the Word YouTube Playlist. Be sure to mark your calendars for major WordPress events in 2025: WordCamp Asia (Manila, Philippines), WordCamp Europe (Basel, Switzerland), and WordCamp US (Portland, Oregon, USA).
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18205";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:4;a:6:{s:4:"data";s:57:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:33:"Write Books With the Block Editor";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:69:"https://wordpress.org/news/2024/12/write-books-with-the-block-editor/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Mon, 16 Dec 2024 08:36:57 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18176";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:327:"If you need a little push to start writing this winter, in the comfort of your familiar editor, here it is! You can now use the Block Editor to create electronic books and other documents—all completely offline. What a full circle moment for Gutenberg! The Block Editor contains so many features I miss when writing […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:4:"Ella";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:6919:"
If you need a little push to start writing this winter, in the comfort of your familiar editor, here it is! You can now use the Block Editor to create electronic books and other documents—all completely offline. What a full circle moment for Gutenberg!
The Block Editor contains so many features I miss when writing in other editors. It produces clean, semantic markup. You can paste in content from anywhere and the editor will clean it up for you, or paste a link onto selected text to auto-link. The List View and Outline panels allow you to easily navigate and inspect the content. And we’re constantly iterating on the Block Editor: more features and improvements are on the way, such as refined drag and drop interactions coming in early 2025.
All this inspired me to wrap our editor in an app that can read and write local files—just as other document editors do. It turns out that EPUB is the best file format to store the content, because EPUB is an open standard for e-books that is essentially a ZIP file containing HTML and media—HTML like your WordPress posts!
And just like that, the WordPress Block Editor can also be used to write books! The cool thing about EPUB files is that any e-book app, such as Kindle and Apple Books, can open it. So even if someone doesn’t have this editor, they can still easily read the content, which makes the files it produces portable.
The editor allows you to create a cover, so you can easily distinguish between the books or documents you write. It will also treat each heading as a chapter so you can easily navigate content when opened in an e-book reader.
The term “book” should be taken broadly. While the file that the Block Editor produces is primarily used for e-books, you can create any document with it. It’s possible to export your document to a DOCX file in case you need it, though the more complex blocks are not supported yet.
It is still very much a nascent project. There’s many features left to be added, such as revisions and the ability to open any externally created EPUB files, or even DOCX files, so keep an eye out for these in the coming weeks and months! If you’re interested in this editor, it’s all open source, and I welcome any kind of help.
For now, the demo editor is installable as a Progressive Web App (PWA) in Chrome. While it’s totally usable without installation, it does give you some nice benefits such as allowing you to open the EPUB files directly from your OS. In the future we might wrap it in proper native apps. Your feedback is welcome on GitHub!
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18176";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:5;a:6:{s:4:"data";s:57:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:36:"Openverse.org: A Sight for Sore Eyes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:71:"https://wordpress.org/news/2024/12/openverse-org-a-sight-for-sore-eyes/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 11 Dec 2024 17:45:50 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18168";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:378:"Openverse.org, the vibrant platform for openly licensed media, has introduced a sleek and modern Dark Mode feature. This new site theme is designed to enhance users’ comfort and style as they explore the extensive library of creative resources. Whether for late-night browsing or simply a preference for darker aesthetics, Dark Mode makes engaging with Openverse […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Brett McSherry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:7308:"
Openverse.org, the vibrant platform for openly licensed media, has introduced a sleek and modern Dark Mode feature. This new site theme is designed to enhance users’ comfort and style as they explore the extensive library of creative resources. Whether for late-night browsing or simply a preference for darker aesthetics, Dark Mode makes engaging with Openverse easier on the eyes and more personalized than ever.
By reducing screen brightness in low-light settings, Dark Mode offers a more relaxed viewing experience, helping to minimize eye strain. It also caters to users with light sensitivity, creating a more inclusive browsing environment. This thoughtful addition underscores Openverse’s commitment to delivering tools that are as functional as they are visually appealing.
The release of Dark Mode is part of Openverse’s broader effort to innovate and adapt to the needs of its growing community. From the thoughtful interface design to the careful attention to accessibility, every detail was crafted to reflect Openverse’s mission of empowering creativity. By embracing modern frontend implementations like Dark Mode without compromising usability or accessibility, Openverse continues to grow while honoring the brand’s essence. In addition, this update lays the groundwork for future developments aimed at providing even more customization options and improved user experiences.
“Dark Mode marks an exciting step forward for Openverse. We designed and implemented a new user interface that keeps the brand’s essence while providing the same search experience. We’re thrilled to see how this feature fits within users’ preferences and enhances the creative journey.” – Francisco Vera. Designer
Ready to explore Openverse in a whole new light? Head to Openverse.org today and look for the Dark Mode toggle in the site footer.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18168";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:6;a:6:{s:4:"data";s:57:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:35:"WordPress 6.7.1 Maintenance Release";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:71:"https://wordpress.org/news/2024/11/wordpress-6-7-1-maintenance-release/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Thu, 21 Nov 2024 14:56:31 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:8:"Releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18096";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:349:"WordPress 6.7.1 is now available! This minor release features 16 bug fixes throughout Core and the Block Editor. WordPress 6.7.1 is a fast-follow release with a strict focus on bugs introduced in WordPress 6.7. The next major release will be version 6.8, planned for April 2025. If you have sites that support automatic background updates, […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:19:"Jonathan Desrosiers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:8649:"
WordPress 6.7.1 is a fast-follow release with a strict focus on bugs introduced in WordPress 6.7. The next major release will be version 6.8, planned for April 2025.
If you have sites that support automatic background updates, the update process will begin automatically.
WordPress 6.7.1 would not have been possible without the contributions of the following people. Their asynchronous coordination to deliver maintenance fixes into a stable release is a testament to the power and capability of the WordPress community.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18096";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:7;a:6:{s:4:"data";s:66:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:27:"WordPress 6.7 “Rollins”";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:43:"https://wordpress.org/news/2024/11/rollins/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Tue, 12 Nov 2024 21:35:22 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:4:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:8:"Releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:3:"6.7";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:8:"releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18066";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:422:"WordPress 6.7, code-named \'Rollins,\' celebrates legendary jazz saxophonist Sonny Rollins and debuts the sleek, versatile Twenty Twenty-Five theme, designed for any blog, any scale. Dive into new font management features and gain a macro perspective on your site with the Zoom Out feature. Embrace the spirit of creativity and bold expression that defines Rollins\' music as you explore WordPress 6.7’s latest innovations.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Matt Mullenweg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:71735:"
Each WordPress release celebrates an artist who has made an indelible mark on the world of music. WordPress 6.7, code-named “Rollins,” pays tribute to the legendary jazz saxophonist Sonny Rollins. Known as one of the greatest improvisers and pioneers in jazz, Rollins has influenced generations of musicians with his technical brilliance, innovative spirit, and fearless approach to musical expression.
Sonny Rollins’ work is characterized by its unmatched energy and emotional depth. His compositions, such as “St. Thomas,” “Oleo,” and “Airegin,” are timeless jazz standards, celebrated for their rhythmic complexity and melodic inventiveness. Rollins’ bold and exploratory style resonates with WordPress’ own commitment to empowering creators to push boundaries and explore new possibilities in digital expression.
Embrace the spirit of innovation and spontaneity that defines Rollins’ sound as you dive into the new features and enhancements of WordPress 6.7.
Welcome to WordPress 6.7!
WordPress 6.7 debuts the modern Twenty Twenty-Five theme, offering ultimate design flexibility for any blog at any scale. Control your site typography like never before with new font management features. The new Zoom Out feature lets you design your site with a macro view, stepping back from the details to bring the big picture to life.
Twenty Twenty-Five offers a flexible, design-focused theme that lets you build stunning sites with ease. Tailor your aesthetic with an array of style options, block patterns, and color palettes. Pared down to the essentials, this is a theme that can truly grow with you.
Get the big picture with Zoom Out
Explore your content from a new perspective
Edit and arrange entire sections of your content like never before. A broader view of your site lets you add, edit, shuffle, or remove patterns to your liking. Embrace your inner architect.
Connect blocks and custom fields with no hassle (or code)
A streamlined way to create dynamic content
This feature introduces a new UI for connecting blocks to custom fields, putting control of dynamic content directly in the editor. Link blocks with fields in just a few clicks, enhancing flexibility and efficiency when building. Your clients will love you—as if they didn’t already.
Embrace your inner font nerd
New style section, new possibilities
Create, edit, remove, and apply font size presets with the next addition to the Styles interface. Override theme defaults or create your own custom font size, complete with fluid typography for responsive font scaling. Get into the details!
Performance
WordPress 6.7 delivers important performance updates, including faster pattern loading, optimized previews in the data views component, improved PHP 8+ support and removal of deprecated code, auto sizes for lazy-loaded images, and more efficient tag processing in the HTML API.
Accessibility
65+ accessibility fixes and enhancements focus on foundational aspects of the WordPress experience, from improving user interface components and keyboard navigation in the Editor, to an accessible heading on WordPress login screens and clearer labeling throughout.
And much more
For a comprehensive overview of all the new features and enhancements in WordPress 6.7, please visit the feature-showcase website.
Learn WordPress is a free resource for new and experienced WordPress users. Learn is stocked with how-to videos on using various features in WordPress, interactive workshops for exploring topics in-depth, and lesson plans for diving deep into specific areas of WordPress.
Read the WordPress 6.7 Release Notes for information on installation, enhancements, fixed issues, release contributors, learning resources, and the list of file changes.
Explore the WordPress 6.7 Field Guide. Learn about the changes in this release with detailed developer notes to help you build with WordPress.
The 6.7 release squad
Every release comes to you from a dedicated team of enthusiastic contributors who help keep things on track and moving smoothly. The team that has led 6.7 is a cross-functional group of contributors who are always ready to champion ideas, remove blockers, and resolve issues.
WordPress 6.7 reflects the tireless efforts and passion of more than 780 contributors in countries all over the world. This release also welcomed over 230 first-time contributors!
Their collaboration delivered more than 340 enhancements and fixes, ensuring a stable release for all—a testament to the power and capability of the WordPress open source community.
More than 40 locales have fully translated WordPress 6.7 into their language making this one of the most translated releases ever on day one. Community translators are working hard to ensure more translations are on their way. Thank you to everyone who helps make WordPress available in 200 languages.
Last but not least, thanks to the volunteers who contribute to the support forums by answering questions from WordPress users worldwide.
Get involved
Participation in WordPress goes far beyond coding, and learning more and getting involved is easy. Discover the teams that come together to Make WordPress and use this interactive tool to help you decide which is right for you.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18066";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:8;a:6:{s:4:"data";s:69:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:33:"WordPress 6.7 Release Candidate 3";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:69:"https://wordpress.org/news/2024/11/wordpress-6-7-release-candidate-3/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Tue, 05 Nov 2024 17:02:15 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:5:{i:0;a:5:{s:4:"data";s:11:"Development";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:8:"Releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:3:"6.7";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"development";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:8:"releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18056";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:360:"The third release candidate (RC3) for WordPress 6.7 is ready for download and testing! This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC3 on a test server and site. Reaching this phase […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"David Baumwald";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:11405:"
The third release candidate (RC3) for WordPress 6.7 is ready for download and testing!
This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC3 on a test server and site.
Reaching this phase of the release cycle is an important milestone. While release candidates are considered ready for release, testing remains crucial to ensure that everything in WordPress 6.7 is the best it can be.
You can test WordPress 6.7 RC3 in four ways:
Plugin
Install and activate the WordPress Beta Tester plugin on a WordPress install. (Select the “Bleeding edge” channel and “Beta/RC Only” stream).
Direct Download
Download the RC3 version (zip) and install it on a WordPress website.
Command Line
Use the following WP-CLI command: wp core update --version=6.7-RC3
WordPress Playground
Use the 6.7 RC3 WordPress Playground instance (available within 35 minutes after the release is ready) to test the software directly in your browser without the need for a separate site or setup.
Get a recap of WordPress 6.7’s highlighted features in the Beta 1 announcement. For more technical information related to issues addressed since RC2, you can browse the following links:
WordPress is open source software made possible by a passionate community of people collaborating on and contributing to its development. The resources below outline various ways you can help the world’s most popular open source web platform, regardless of your technical expertise.
Get involved in testing
Testing for issues is critical to ensuring WordPress is performant and stable. It’s also a meaningful way for anyone to contribute. This detailed guide will walk you through testing features in WordPress 6.7. For those new to testing, follow this general testing guide for more details on getting set up.
If you encounter an issue, please report it to the Alpha/Beta area of the support forums or directly to WordPress Trac if you are comfortable writing a reproducible bug report. You can also check your issue against a list of known bugs.
For plugin and theme authors, your products play an integral role in extending the functionality and value of WordPress for all users.
Thanks for continuing to test your themes and plugins with the WordPress 6.7 beta releases. With RC3, you’ll want to conclude your testing and update the “Tested up to” version in your plugin’s readme file to 6.7.
If you find compatibility issues, please post detailed information to the support forum.
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18056";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:9;a:6:{s:4:"data";s:69:"
";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:33:"WordPress 6.7 Release Candidate 2";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:69:"https://wordpress.org/news/2024/10/wordpress-6-7-release-candidate-2/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Tue, 29 Oct 2024 17:08:31 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:5:{i:0;a:5:{s:4:"data";s:11:"Development";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:8:"Releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:3:"6.7";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"development";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:8:"releases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18043";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:361:"The second release candidate (RC2) for WordPress 6.7 is ready for download and testing! This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC2 on a test server and site. Reaching this phase […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"David Baumwald";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:11116:"
The second release candidate (RC2) for WordPress 6.7 is ready for download and testing!
This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC2 on a test server and site.
Reaching this phase of the release cycle is an important milestone. While release candidates are considered ready for release, testing remains crucial to ensure that everything in WordPress 6.7 is the best it can be.
You can test WordPress 6.7 RC2 in four ways:
Plugin
Install and activate the WordPress Beta Tester plugin on a WordPress install. (Select the “Bleeding edge” channel and “Beta/RC Only” stream).
Direct Download
Download the RC2 version (zip) and install it on a WordPress website.
Command Line
Use the following WP-CLI command: wp core update --version=6.7-RC2
WordPress Playground
Use the 6.7 RC2 WordPress Playground instance (available within 35 minutes after the release is ready) to test the software directly in your browser without the need for a separate site or setup.
Get a recap of WordPress 6.7’s highlighted features in the Beta 1 announcement. For more technical information related to issues addressed since RC1, you can browse the following links:
WordPress is open source software made possible by a passionate community of people collaborating on and contributing to its development. The resources below outline various ways you can help the world’s most popular open source web platform, regardless of your technical expertise.
Get involved in testing
Testing for issues is critical to ensuring WordPress is performant and stable. It’s also a meaningful way for anyone to contribute. This detailed guide will walk you through testing features in WordPress 6.7. For those new to testing, follow this general testing guide for more details on getting set up.
If you encounter an issue, please report it to the Alpha/Beta area of the support forums or directly to WordPress Trac if you are comfortable writing a reproducible bug report. You can also check your issue against a list of known bugs.
For plugin and theme authors, your products play an integral role in extending the functionality and value of WordPress for all users.
Thanks for continuing to test your themes and plugins with the WordPress 6.7 beta releases. With RC2, you’ll want to conclude your testing and update the “Tested up to” version in your plugin’s readme file to 6.7.
If you find compatibility issues, please post detailed information to the support forum.
Lorem ipsum dolor sit amet, cu usu cibo vituperata, id ius probo maiestatis inciderint, sit eu vide volutpat.
'),
(1388, 1674, 'sa_slide8_image_data', '~left top~contain~no-repeat~#ead1dc'),
(1389, 1674, 'sa_slide8_link_url', ''),
(1390, 1674, 'sa_slide8_link_target', '_self'),
(1391, 1674, 'sa_slide8_popup_type', 'NONE'),
(1392, 1674, 'sa_slide8_popup_imageid', ''),
(1393, 1674, 'sa_slide8_popup_imagetitle', ''),
(1394, 1674, 'sa_slide8_popup_video_id', ''),
(1395, 1674, 'sa_slide8_popup_video_type', ''),
(1396, 1674, 'sa_slide8_popup_background', 'no'),
(1397, 1674, 'sa_slide8_popup_html', ''),
(1398, 1674, 'sa_slide8_popup_shortcode', '0'),
(1399, 1674, 'sa_slide8_popup_bgcol', '#ffffff'),
(1400, 1674, 'sa_slide8_popup_width', '600'),
(1401, 1674, 'sa_disable_visual_editor', '0'),
(1402, 1674, 'sa_num_slides', '8'),
(1403, 1674, 'sa_slide_duration', '4'),
(1404, 1674, 'sa_slide_transition', '0.3'),
(1405, 1674, 'sa_slide_by', '1'),
(1406, 1674, 'sa_loop_slider', '1'),
(1407, 1674, 'sa_stop_hover', '1'),
(1408, 1674, 'sa_nav_arrows', '1'),
(1409, 1674, 'sa_pagination', '1'),
(1410, 1674, 'sa_shortcodes', '0'),
(1411, 1674, 'sa_random_order', '1'),
(1412, 1674, 'sa_reverse_order', '0'),
(1413, 1674, 'sa_mouse_drag', '0'),
(1414, 1674, 'sa_touch_drag', '1'),
(1415, 1674, 'sa_auto_height', '0'),
(1416, 1674, 'sa_vert_center', '0'),
(1417, 1674, 'sa_items_width1', '1'),
(1418, 1674, 'sa_items_width2', '2'),
(1419, 1674, 'sa_items_width3', '3'),
(1420, 1674, 'sa_items_width4', '4'),
(1421, 1674, 'sa_items_width5', '4'),
(1422, 1674, 'sa_items_width6', '4'),
(1423, 1674, 'sa_transition', 'fade'),
(1424, 1674, 'sa_hero_slider', '0'),
(1425, 1674, 'sa_showcase_slider', '0'),
(1426, 1674, 'sa_showcase_width', '120'),
(1427, 1674, 'sa_showcase_tablet', '1'),
(1428, 1674, 'sa_showcase_width_tab', '130'),
(1429, 1674, 'sa_showcase_mobile', '0'),
(1430, 1674, 'sa_showcase_width_mob', '140'),
(1431, 1674, 'sa_css_id', 'sample_slider'),
(1432, 1674, 'sa_background_color', '#fafafa'),
(1433, 1674, 'sa_border_width', '1'),
(1434, 1674, 'sa_border_color', '#f0f0f0'),
(1435, 1674, 'sa_border_radius', '5'),
(1436, 1674, 'sa_wrapper_padd_top', '8'),
(1437, 1674, 'sa_wrapper_padd_right', '8'),
(1438, 1674, 'sa_wrapper_padd_bottom', '8'),
(1439, 1674, 'sa_wrapper_padd_left', '8'),
(1440, 1674, 'sa_slide_min_height_perc', '50'),
(1441, 1674, 'sa_slide_padding_tb', '5'),
(1442, 1674, 'sa_slide_padding_lr', '5'),
(1443, 1674, 'sa_slide_margin_lr', '0'),
(1444, 1674, 'sa_autohide_arrows', '1'),
(1445, 1674, 'sa_dot_per_slide', '0'),
(1446, 1674, 'sa_slide_icons_location', 'Center Center'),
(1447, 1674, 'sa_slide_icons_visible', '0'),
(1448, 1674, 'sa_slide_icons_color', 'white'),
(1449, 1674, 'sa_thumbs_active', '0'),
(1450, 1674, 'sa_thumbs_location', 'Inside Bottom'),
(1451, 1674, 'sa_thumbs_image_size', 'thumbnail'),
(1452, 1674, 'sa_thumbs_padding', '3'),
(1453, 1674, 'sa_thumbs_width', '150'),
(1454, 1674, 'sa_thumbs_height', '85'),
(1455, 1674, 'sa_thumbs_opacity', '50'),
(1456, 1674, 'sa_thumbs_border_width', '0'),
(1457, 1674, 'sa_thumbs_border_color', '#ffffff'),
(1458, 1674, 'sa_thumbs_resp_tablet', '75'),
(1459, 1674, 'sa_thumbs_resp_mobile', '50'),
(1464, 1683, '_wp_attached_file', '2020/09/clinica_cloud-v2.pdf'),
(1465, 1684, '_wp_attached_file', '2020/09/clinica_neuroimaging-v2.pdf'),
(1466, 1694, '_wp_attached_file', '2020/09/icon_ARAMISLAB_rvb.png'),
(1467, 1694, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:347;s:6:"height";i:347;s:4:"file";s:30:"2020/09/icon_ARAMISLAB_rvb.png";s:5:"sizes";a:3:{s:9:"thumbnail";a:4:{s:4:"file";s:30:"icon_ARAMISLAB_rvb-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:6:"medium";a:4:{s:4:"file";s:30:"icon_ARAMISLAB_rvb-300x300.png";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:9:"image/png";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:30:"icon_ARAMISLAB_rvb-272x182.png";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1471, 1698, '_wp_attached_file', '2018/11/alexandre_routier-1.jpg'),
(1472, 1698, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:300;s:6:"height";i:300;s:4:"file";s:31:"2018/11/alexandre_routier-1.jpg";s:5:"sizes";a:2:{s:9:"thumbnail";a:4:{s:4:"file";s:31:"alexandre_routier-1-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:31:"alexandre_routier-1-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:3:"6.3";s:6:"credit";s:0:"";s:6:"camera";s:12:"Canon EOS 6D";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1594652897";s:9:"copyright";s:0:"";s:12:"focal_length";s:3:"200";s:3:"iso";s:3:"500";s:13:"shutter_speed";s:7:"0.00125";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1473, 1699, '_wp_attached_file', '2018/11/juliette_ortholand.jpg'),
(1474, 1699, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:512;s:6:"height";i:512;s:4:"file";s:30:"2018/11/juliette_ortholand.jpg";s:5:"sizes";a:3:{s:9:"thumbnail";a:4:{s:4:"file";s:30:"juliette_ortholand-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:30:"juliette_ortholand-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:30:"juliette_ortholand-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1475, 1700, '_wp_attached_file', '2018/11/cecile_di_folco.jpg'),
(1476, 1700, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1920;s:6:"height";i:1371;s:4:"file";s:27:"2018/11/cecile_di_folco.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"cecile_di_folco-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"cecile_di_folco-300x214.jpg";s:5:"width";i:300;s:6:"height";i:214;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"cecile_di_folco-768x548.jpg";s:5:"width";i:768;s:6:"height";i:548;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"cecile_di_folco-1024x731.jpg";s:5:"width";i:1024;s:6:"height";i:731;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:27:"cecile_di_folco-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1477, 1702, '_wp_attached_file', '2018/11/daniel_racoceanu.jpg'),
(1478, 1702, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:528;s:6:"height";i:550;s:4:"file";s:28:"2018/11/daniel_racoceanu.jpg";s:5:"sizes";a:3:{s:9:"thumbnail";a:4:{s:4:"file";s:28:"daniel_racoceanu-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:28:"daniel_racoceanu-288x300.jpg";s:5:"width";i:288;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:28:"daniel_racoceanu-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"5";s:6:"credit";s:0:"";s:6:"camera";s:7:"DMC-GX1";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1384253936";s:9:"copyright";s:0:"";s:12:"focal_length";s:2:"26";s:3:"iso";s:3:"800";s:13:"shutter_speed";s:17:"0.016666666666667";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(1479, 1703, '_wp_attached_file', '2018/11/clement_mantoux.jpg'),
(1480, 1703, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:343;s:6:"height";i:365;s:4:"file";s:27:"2018/11/clement_mantoux.jpg";s:5:"sizes";a:3:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"clement_mantoux-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"clement_mantoux-282x300.jpg";s:5:"width";i:282;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:27:"clement_mantoux-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1481, 1704, '_wp_attached_file', '2018/11/pierre_emmanuel_poulet.jpg'),
(1482, 1704, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1920;s:6:"height";i:2880;s:4:"file";s:34:"2018/11/pierre_emmanuel_poulet.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:34:"pierre_emmanuel_poulet-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:34:"pierre_emmanuel_poulet-200x300.jpg";s:5:"width";i:200;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:35:"pierre_emmanuel_poulet-768x1152.jpg";s:5:"width";i:768;s:6:"height";i:1152;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:35:"pierre_emmanuel_poulet-683x1024.jpg";s:5:"width";i:683;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:34:"pierre_emmanuel_poulet-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1483, 1705, '_wp_attached_file', '2018/11/arthur_desbois.jpg'),
(1484, 1705, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:721;s:6:"height";i:721;s:4:"file";s:26:"2018/11/arthur_desbois.jpg";s:5:"sizes";a:3:{s:9:"thumbnail";a:4:{s:4:"file";s:26:"arthur_desbois-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:26:"arthur_desbois-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:26:"arthur_desbois-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1485, 1707, '_wp_attached_file', '2018/11/charleypresigny.jpg'),
(1486, 1707, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:4160;s:6:"height";i:3120;s:4:"file";s:27:"2018/11/charleypresigny.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"charleypresigny-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"charleypresigny-300x225.jpg";s:5:"width";i:300;s:6:"height";i:225;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"charleypresigny-768x576.jpg";s:5:"width";i:768;s:6:"height";i:576;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"charleypresigny-1024x768.jpg";s:5:"width";i:1024;s:6:"height";i:768;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:27:"charleypresigny-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:3:"1.8";s:6:"credit";s:0:"";s:6:"camera";s:7:"JAT-L41";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1599225186";s:9:"copyright";s:0:"";s:12:"focal_length";s:4:"3.62";s:3:"iso";s:3:"125";s:13:"shutter_speed";s:8:"0.008111";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(1487, 1709, '_wp_attached_file', '2020/10/DataViz_Job_offer.pdf'),
(1488, 1709, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:9:"thumbnail";a:4:{s:4:"file";s:33:"DataViz_Job_offer-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:33:"DataViz_Job_offer-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:34:"DataViz_Job_offer-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:4:"full";a:4:{s:4:"file";s:25:"DataViz_Job_offer-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(1489, 1710, '_wp_attached_file', '2020/10/DataViz_Job_offer-1.pdf'),
(1490, 1710, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:9:"thumbnail";a:4:{s:4:"file";s:35:"DataViz_Job_offer-1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:35:"DataViz_Job_offer-1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:36:"DataViz_Job_offer-1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:4:"full";a:4:{s:4:"file";s:27:"DataViz_Job_offer-1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(1491, 1712, '_wp_attached_file', '2018/11/ravihassanaly.jpg'),
(1492, 1712, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:960;s:6:"height";i:960;s:4:"file";s:25:"2018/11/ravihassanaly.jpg";s:5:"sizes";a:4:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"ravihassanaly-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:25:"ravihassanaly-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:25:"ravihassanaly-768x768.jpg";s:5:"width";i:768;s:6:"height";i:768;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:25:"ravihassanaly-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1493, 1714, '_wp_attached_file', '2018/11/benoitsauty.jpg'),
(1494, 1714, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1360;s:6:"height";i:1360;s:4:"file";s:23:"2018/11/benoitsauty.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:23:"benoitsauty-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:23:"benoitsauty-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:23:"benoitsauty-768x768.jpg";s:5:"width";i:768;s:6:"height";i:768;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:25:"benoitsauty-1024x1024.jpg";s:5:"width";i:1024;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:23:"benoitsauty-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1495, 1715, '_wp_attached_file', '2018/11/tristanvenot.jpg'),
(1496, 1715, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:660;s:6:"height";i:1148;s:4:"file";s:24:"2018/11/tristanvenot.jpg";s:5:"sizes";a:4:{s:9:"thumbnail";a:4:{s:4:"file";s:24:"tristanvenot-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:24:"tristanvenot-172x300.jpg";s:5:"width";i:172;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:25:"tristanvenot-589x1024.jpg";s:5:"width";i:589;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:24:"tristanvenot-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1497, 1716, '_wp_attached_file', '2018/11/vitodichio.jpg'),
(1498, 1716, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1280;s:6:"height";i:853;s:4:"file";s:22:"2018/11/vitodichio.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:22:"vitodichio-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:22:"vitodichio-300x200.jpg";s:5:"width";i:300;s:6:"height";i:200;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:22:"vitodichio-768x512.jpg";s:5:"width";i:768;s:6:"height";i:512;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:23:"vitodichio-1024x682.jpg";s:5:"width";i:1024;s:6:"height";i:682;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:22:"vitodichio-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1499, 1717, '_wp_attached_file', '2018/11/sophieskriabine.jpg'),
(1500, 1717, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1920;s:6:"height";i:2478;s:4:"file";s:27:"2018/11/sophieskriabine.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"sophieskriabine-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"sophieskriabine-232x300.jpg";s:5:"width";i:232;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"sophieskriabine-768x991.jpg";s:5:"width";i:768;s:6:"height";i:991;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"sophieskriabine-793x1024.jpg";s:5:"width";i:793;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:27:"sophieskriabine-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1501, 1718, '_wp_attached_file', '2018/11/remybenmessaoud.jpg'),
(1502, 1718, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1920;s:6:"height";i:1920;s:4:"file";s:27:"2018/11/remybenmessaoud.jpg";s:5:"sizes";a:5:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"remybenmessaoud-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"remybenmessaoud-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"remybenmessaoud-768x768.jpg";s:5:"width";i:768;s:6:"height";i:768;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:29:"remybenmessaoud-1024x1024.jpg";s:5:"width";i:1024;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:20:"sow-carousel-default";a:4:{s:4:"file";s:27:"remybenmessaoud-272x182.jpg";s:5:"width";i:272;s:6:"height";i:182;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1562, 1733, '_edit_lock', '1610714202:8'),
(1563, 1733, '_edit_last', '8'),
(1564, 1748, '_edit_lock', '1610443611:8'),
(1565, 1748, '_edit_last', '8'),
(1585, 1751, '_wp_attached_file', '2020/09/cropped-icon_ARAMISLAB_rvb.png'),
(1586, 1751, '_wp_attachment_context', 'custom-logo'),
(1587, 1751, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:347;s:6:"height";i:347;s:4:"file";s:38:"2020/09/cropped-icon_ARAMISLAB_rvb.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:38:"cropped-icon_ARAMISLAB_rvb-300x300.png";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:38:"cropped-icon_ARAMISLAB_rvb-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1616, 1758, '_menu_item_type', 'post_type'),
(1617, 1758, '_menu_item_menu_item_parent', '0'),
(1618, 1758, '_menu_item_object_id', '1098'),
(1619, 1758, '_menu_item_object', 'page'),
(1620, 1758, '_menu_item_target', ''),
(1621, 1758, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1622, 1758, '_menu_item_xfn', ''),
(1623, 1758, '_menu_item_url', ''),
(1625, 1759, '_menu_item_type', 'post_type'),
(1626, 1759, '_menu_item_menu_item_parent', '0'),
(1627, 1759, '_menu_item_object_id', '620'),
(1628, 1759, '_menu_item_object', 'page'),
(1629, 1759, '_menu_item_target', ''),
(1630, 1759, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1631, 1759, '_menu_item_xfn', ''),
(1632, 1759, '_menu_item_url', ''),
(1634, 1760, '_menu_item_type', 'post_type'),
(1635, 1760, '_menu_item_menu_item_parent', '0'),
(1636, 1760, '_menu_item_object_id', '30'),
(1637, 1760, '_menu_item_object', 'page'),
(1638, 1760, '_menu_item_target', ''),
(1639, 1760, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1640, 1760, '_menu_item_xfn', ''),
(1641, 1760, '_menu_item_url', ''),
(1643, 1761, '_menu_item_type', 'post_type'),
(1644, 1761, '_menu_item_menu_item_parent', '0'),
(1645, 1761, '_menu_item_object_id', '26'),
(1646, 1761, '_menu_item_object', 'page'),
(1647, 1761, '_menu_item_target', ''),
(1648, 1761, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1649, 1761, '_menu_item_xfn', ''),
(1650, 1761, '_menu_item_url', ''),
(1652, 1762, '_menu_item_type', 'post_type'),
(1653, 1762, '_menu_item_menu_item_parent', '0'),
(1654, 1762, '_menu_item_object_id', '22'),
(1655, 1762, '_menu_item_object', 'page'),
(1656, 1762, '_menu_item_target', ''),
(1657, 1762, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1658, 1762, '_menu_item_xfn', ''),
(1659, 1762, '_menu_item_url', ''),
(1661, 1763, '_menu_item_type', 'post_type'),
(1662, 1763, '_menu_item_menu_item_parent', '1762'),
(1663, 1763, '_menu_item_object_id', '4'),
(1664, 1763, '_menu_item_object', 'page'),
(1665, 1763, '_menu_item_target', ''),
(1666, 1763, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1667, 1763, '_menu_item_xfn', ''),
(1668, 1763, '_menu_item_url', ''),
(1670, 1764, '_menu_item_type', 'custom'),
(1671, 1764, '_menu_item_menu_item_parent', '0'),
(1672, 1764, '_menu_item_object_id', '1764'),
(1673, 1764, '_menu_item_object', 'custom'),
(1674, 1764, '_menu_item_target', ''),
(1675, 1764, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1676, 1764, '_menu_item_xfn', ''),
(1677, 1764, '_menu_item_url', '#'),
(1679, 1765, '_menu_item_type', 'post_type'),
(1680, 1765, '_menu_item_menu_item_parent', '1764'),
(1681, 1765, '_menu_item_object_id', '26'),
(1682, 1765, '_menu_item_object', 'page'),
(1683, 1765, '_menu_item_target', ''),
(1684, 1765, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1685, 1765, '_menu_item_xfn', ''),
(1686, 1765, '_menu_item_url', ''),
(1706, 1748, 'title', 'Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis'),
(1707, 1748, '_title', 'field_5ff86849c7493'),
(1708, 1748, 'authors', 'Virgilio Kmetzsch; Vincent Anquetil; Dario Saracino; Daisy Rinaldi; Agnès Camuzat; Thomas Gareau; Ludmila Jornea; Sylvie Forlani; Philippe Couratier; David Wallon; Florence Pasquier; Noémie Robil; Pierre de la Grange; Ivan Moszer; Isabelle Le Ber; Olivier Colliot; Emmanuelle Becker; The PREV-DEMALS study group '),
(1709, 1748, '_authors', 'field_5ff868da50eec'),
(1710, 1748, 'date', '20201125'),
(1711, 1748, '_date', 'field_5ff868f750eed'),
(1712, 1748, 'journal', 'Journal of Neurology, Neurosurgery and Psychiatry'),
(1713, 1748, '_journal', 'field_5ff8692750eee'),
(1714, 1748, 'keywords', 'a:4:{i:0;s:2:"22";i:1;s:2:"23";i:2;s:2:"24";i:3;s:2:"25";}'),
(1715, 1748, '_keywords', 'field_5ff8695650eef'),
(1716, 1748, 'link', 'http://dx.doi.org/10.1136/jnnp-2020-324647'),
(1717, 1748, '_link', 'field_5ff8696250ef0'),
(1718, 1748, 'abstract_text', 'Objective: To identify potential biomarkers of preclinical and clinical progression in chromosome 9 open reading frame 72 gene (C9orf72)-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72 patients and presymptomatic carriers. Methods: The PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing. The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers. Results: Four miRNAs were differentially expressed between patients and controls: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in patients. MiR-34a-5p was also overexpressed in presymptomatic carriers compared with healthy controls, suggesting that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Moreover, miR-345-5p was also overexpressed in patients compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Together, miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease. Four presymptomatic subjects in transitional/prodromal stage, close to the disease conversion, exhibited a stronger similarity with the expression levels of patients. Conclusions: We identified a signature of four miRNAs differentially expressed in plasma between clinical conditions that have potential to represent progression biomarkers for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. This study suggests that dysregulation of miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.'),
(1719, 1748, '_abstract_text', 'field_5ff869ab50ef3'),
(1720, 1748, 'description_text', 'Description is here'),
(1721, 1748, '_description_text', 'field_5ff869cb50ef5'),
(1722, 1748, '_wp_old_slug', 'plasma-microrna-signature-in-presymptomatic-and-symptomatic-subjects-with-c9orf72-associated-frontotemporal-dementia-and-amyotrophic-lateral-sclerosis'),
(1731, 1748, 'abstract', 'Objective: To identify potential biomarkers of preclinical and clinical progression in chromosome 9 open reading frame 72 gene (C9orf72)-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72patients and presymptomatic carriers.
Methods: The PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72 mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing. The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers.
Results: Four miRNAs were differentially expressed between patients and controls: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in patients. MiR-34a-5p was also overexpressed in presymptomatic carriers compared with healthy controls, suggesting that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Moreover, miR-345-5p was also overexpressed in patients compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Together, miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease. Four presymptomatic subjects in transitional/prodromal stage, close to the disease conversion, exhibited a stronger similarity with the expression levels of patients.
Conclusions: We identified a signature of four miRNAs differentially expressed in plasma between clinical conditions that have potential to represent progression biomarkers for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. This study suggests that dysregulation of miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.'),
(1732, 1748, '_abstract', 'field_5ff869ab50ef3'),
(1733, 1748, 'description', 'Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are devastating neurodegenerative diseases. They may have a common genetic cause, the most typical being a mutation in the C9orf72 gene. Since there is no cure so far, it is essential to identify biomarkers of disease progression that could be used to evaluate the efficacy of clinical trials. The goal of this study was to assess the use of microRNAs (small molecules that regulate gene expression) found in blood plasma as progression biomarkers of FTD/ALS. We analysed blood samples from 115 subjects (FTD/ALS patients, presymptomatic mutation carriers and healthy controls) and identified four differentially expressed microRNAs. We then built models based on the expression levels of this microRNA signature, which were able to predict if a new sample came from a patient, a presymptomatic carrier or a healthy control. Our results highlight the potential of plasma microRNAs as progression biomarkers for FTD/ALS, which could provide a non-invasive method to monitor new disease-modifying therapies.'),
(1734, 1748, '_description', 'field_5ff869cb50ef5'),
(1738, 1778, '_edit_lock', '1645202621:10'),
(1739, 1778, '_edit_last', '8'),
(1740, 1778, '_wp_page_template', 'latest-publications.php'),
(1741, 1782, '_menu_item_type', 'post_type'),
(1742, 1782, '_menu_item_menu_item_parent', '1764'),
(1743, 1782, '_menu_item_object_id', '1778'),
(1744, 1782, '_menu_item_object', 'page'),
(1745, 1782, '_menu_item_target', ''),
(1746, 1782, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'),
(1747, 1782, '_menu_item_xfn', ''),
(1748, 1782, '_menu_item_url', ''),
(1752, 1791, '_wp_attached_file', '2021/01/Figure-1-Boxplots.png'),
(1753, 1791, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1309;s:6:"height";i:1025;s:4:"file";s:29:"2021/01/Figure-1-Boxplots.png";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-300x235.png";s:5:"width";i:300;s:6:"height";i:235;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:30:"Figure-1-Boxplots-1024x802.png";s:5:"width";i:1024;s:6:"height";i:802;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-768x601.png";s:5:"width";i:768;s:6:"height";i:601;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1754, 1748, 'Illustration', '1791'),
(1755, 1748, '_Illustration', 'field_5ffcb3a32211f'),
(1756, 1748, 'publisher_link', 'http://dx.doi.org/10.1136/jnnp-2020-324647'),
(1757, 1748, '_publisher_link', 'field_5ff8696250ef0'),
(1758, 1748, 'open_access_link', ''),
(1759, 1748, '_open_access_link', 'field_5ffca31d9e5f6'),
(1778, 1748, 'image', '1791'),
(1779, 1748, '_image', 'field_5ffcb3a32211f'),
(1781, 1797, '_edit_lock', '1618908385:8'),
(1782, 1797, '_edit_last', '11'),
(1784, 1799, '_wp_attached_file', '2021/01/Figure_architectures-scaled.jpg'),
(1785, 1799, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:2560;s:6:"height";i:1541;s:4:"file";s:39:"2021/01/Figure_architectures-scaled.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:32:"Figure_architectures-300x181.jpg";s:5:"width";i:300;s:6:"height";i:181;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:33:"Figure_architectures-1024x616.jpg";s:5:"width";i:1024;s:6:"height";i:616;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:32:"Figure_architectures-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:32:"Figure_architectures-768x462.jpg";s:5:"width";i:768;s:6:"height";i:462;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:33:"Figure_architectures-1536x925.jpg";s:5:"width";i:1536;s:6:"height";i:925;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:34:"Figure_architectures-2048x1233.jpg";s:5:"width";i:2048;s:6:"height";i:1233;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}s:14:"original_image";s:24:"Figure_architectures.jpg";}'),
(1786, 1797, 'title', 'Deep learning for brain disorders: from data processing to disease treatment'),
(1787, 1797, '_title', 'field_5ff86849c7493'),
(1788, 1797, 'authors', 'Ninon Burgos; Simona Bottani; Johann Faouzi; Elina Thibeau-Sutre; Olivier Colliot'),
(1789, 1797, '_authors', 'field_5ff868da50eec'),
(1790, 1797, 'date', '20201215'),
(1791, 1797, '_date', 'field_5ff868f750eed'),
(1792, 1797, 'journal', 'Briefings in Bioinformatics'),
(1793, 1797, '_journal', 'field_5ff8692750eee'),
(1794, 1797, 'keywords', 'a:4:{i:0;s:2:"26";i:1;s:2:"27";i:2;s:2:"28";i:3;s:2:"29";}'),
(1795, 1797, '_keywords', 'field_5ff8695650eef'),
(1796, 1797, 'publisher_link', 'https://doi.org/10.1093/bib/bbaa310'),
(1797, 1797, '_publisher_link', 'field_5ff8696250ef0'),
(1798, 1797, 'open_access_link', 'https://hal.inria.fr/hal-03070554'),
(1799, 1797, '_open_access_link', 'field_5ffca31d9e5f6'),
(1800, 1797, 'image', '1799'),
(1801, 1797, '_image', 'field_5ffcb3a32211f'),
(1802, 1797, 'abstract', 'In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state-of-the-art in numerous fields including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.
Summary key points
Deep learning has been applied to various tasks related to brain disorders, such as image reconstruction, synthesis and segmentation, or disease diagnosis and outcome prediction.
Convolutional neural networks have been successfully applied to imaging and genetic data in numerous brain disorders, while recurrent neural networks showed encouraging results with longitudinal clinical data and sensor data.
Despite the promising results obtained with deep learning, several important limitations need addressing before an application in clinical routine becomes possible.
Future research should especially focus on the generalizability and interpretability of deep learning models.
'),
(1803, 1797, '_abstract', 'field_5ff869ab50ef3'),
(1804, 1797, 'description', 'This review will enable readers to grasp the full potential of deep learning for brain disorders as it presents the main uses of deep learning all along the medical data analysis chain: from data acquisition to disease treatment. We first focus on data processing, covering image reconstruction, signal enhancement and cross-modality image synthesis, and on the biomarkers that can be extracted from spatio-temporal neuroimaging data, such as the volume of normal structures or of lesions. We then describe how deep learning can be used to detect diseases, predict their evolution, improve their understanding and help develop treatments. For these applications, we emphasize the types of architectures and data used, as well as the concerned disorders. Finally, we highlight trending applications and provide guidelines to bridge the gap between research studies and clinical routine.'),
(1805, 1797, '_description', 'field_5ff869cb50ef5'),
(1809, 1748, 'caption', 'This is the description of the image!'),
(1810, 1748, '_caption', 'field_5ffd5f2b99c98'),
(1811, 1797, 'caption', 'Common deep learning architectures for brain disorders. a) U-Net is the most popular architecture for biomedical image segmentation. U-Net architectures have also been used for image reconstruction and synthesis. b) Autoencoders have been used for disease detection, prediction of treatment and integration of multimodal data. c) Variational autoencoders have been used for image segmentation, disease detection and disease subtyping. d) Generative adversarial networks can be used for data augmentation. e) Conditional generative adversarial networks have been used for signal enhancement, image synthesis and disease prediction.'),
(1812, 1797, '_caption', 'field_5ffd5f2b99c98'),
(1841, 1803, '_edit_lock', '1610724218:8'),
(1842, 1804, '_wp_attached_file', '2021/01/Image_1_A-Reliable-and-Rapid-Language-Tool-for-the-Diagnosis-Classification-and-Follow-Up-of-Primary-Progressive-Aphasia-Variants.tif'),
(1843, 1803, '_edit_last', '8'),
(1844, 1803, 'title', 'A Reliable and Rapid Language Tool for the Diagnosis, Classification, and Follow-Up of Primary Progressive Aphasia Variants'),
(1845, 1803, '_title', 'field_5ff86849c7493'),
(1846, 1803, 'authors', 'Stéphane Epelbaum, Yasmina Michel Saade, Constance Flamand Roze, Emmanuel Roze, Sophie Ferrieux, Céline Arbizu, Marie Nogues, Carole Azuar,Bruno Dubois, Sophie Tezenas du Montcel and Marc Teichmann'),
(1847, 1803, '_authors', 'field_5ff868da50eec'),
(1848, 1803, 'date', '20210105'),
(1849, 1803, '_date', 'field_5ff868f750eed'),
(1850, 1803, 'journal', 'Frontiers in Neurology '),
(1851, 1803, '_journal', 'field_5ff8692750eee'),
(1852, 1803, 'keywords', 'a:3:{i:0;s:2:"30";i:1;s:2:"27";i:2;s:2:"31";}'),
(1853, 1803, '_keywords', 'field_5ff8695650eef'),
(1854, 1803, 'publisher_link', 'https://www.frontiersin.org/articles/10.3389/fneur.2020.571657/full'),
(1855, 1803, '_publisher_link', 'field_5ff8696250ef0'),
(1856, 1803, 'open_access_link', 'https://hal.archives-ouvertes.fr/hal-03096896/document'),
(1857, 1803, '_open_access_link', 'field_5ffca31d9e5f6'),
(1858, 1803, 'image', '1805'),
(1859, 1803, '_image', 'field_5ffcb3a32211f'),
(1860, 1803, 'caption', 'PARIS-a-reliable-and-Rapid-Language-Tool-for-the-Diagnosis-Classification-and-Follow-Up-of-Primary-Progressive-Aphasia-Variants'),
(1861, 1803, '_caption', 'field_5ffd5f2b99c98'),
(1862, 1803, 'abstract', '
Background: Primary progressive aphasias (PPA) have been investigated by clinical, therapeutic, and fundamental research but examiner-consistent language tests for reliable reproducible diagnosis and follow-up are lacking.
Methods: We developed and evaluated a rapid language test for PPA (“PARIS”) assessing its inter-examiner consistency, its power to detect and classify PPA, and its capacity to identify language decline after a follow-up of 9 months. To explore the reliability and specificity/sensitivity of the test it was applied to PPA patients (N = 36), typical amnesic Alzheimer\'s disease (AD) patients (N = 24) and healthy controls (N = 35), while comparing it to two rapid examiner-consistent language tests used in stroke-induced aphasia (“LAST”, “ART”).
Results: The application duration of the “PARIS” was ~10 min and its inter-rater consistency was of 88%. The three tests distinguished healthy controls from AD and PPA patients but only the “PARIS” reliably separated PPA from AD and allowed for classifying the two most frequent PPA variants: semantic and logopenic PPA. Compared to the “LAST” and “ART,” the “PARIS” also had the highest sensitivity for detecting language decline.
Conclusions: The “PARIS” is an efficient, rapid, and highly examiner-consistent language test for the diagnosis, classification, and follow-up of frequent PPA variants. It might also be a valuable tool for providing end-points in future therapeutic trials on PPA and other neurodegenerative diseases affecting language processing.
'),
(1863, 1803, '_abstract', 'field_5ff869ab50ef3'),
(1864, 1803, 'description', 'We designed a simple and robust neurocognitive test to screen for primary progressive aphasia (PPA), a rare neurodegenerative syndrome, to help the clinicians in distinguishing PPA from the more common Alzheimer\'s disease.'),
(1865, 1803, '_description', 'field_5ff869cb50ef5'),
(1866, 1805, '_wp_attached_file', '2021/01/the-PARIS-scale.jpg'),
(1867, 1805, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1280;s:6:"height";i:720;s:4:"file";s:27:"2021/01/the-PARIS-scale.jpg";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:27:"the-PARIS-scale-300x169.jpg";s:5:"width";i:300;s:6:"height";i:169;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"the-PARIS-scale-1024x576.jpg";s:5:"width";i:1024;s:6:"height";i:576;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:27:"the-PARIS-scale-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"the-PARIS-scale-768x432.jpg";s:5:"width";i:768;s:6:"height";i:432;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1868, 1803, '_wp_old_slug', '1803'),
(1870, 1807, '_edit_lock', '1610813690:11'),
(1871, 1807, '_edit_last', '11'),
(1872, 1807, 'title', 'Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD'),
(1873, 1807, '_title', 'field_5ff86849c7493'),
(1874, 1807, 'authors', 'Federica Cacciamani, Luisa Sambati, Marion Houot, Marie-Odile Habert, Bruno Dubois, Stéphane Epelbaum, on behalf of the INSIGHT-PreAD study group'),
(1875, 1807, '_authors', 'field_5ff868da50eec'),
(1876, 1807, 'date', '20201014'),
(1877, 1807, '_date', 'field_5ff868f750eed'),
(1878, 1807, 'journal', 'Alzheimer\'s Research & Therapy '),
(1879, 1807, '_journal', 'field_5ff8692750eee'),
(1880, 1807, 'keywords', 'a:3:{i:0;s:2:"30";i:1;s:2:"32";i:2;s:2:"33";}'),
(1881, 1807, '_keywords', 'field_5ff8695650eef'),
(1882, 1807, 'publisher_link', 'https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00700-8'),
(1883, 1807, '_publisher_link', 'field_5ff8696250ef0'),
(1884, 1807, 'open_access_link', 'https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00700-8'),
(1885, 1807, '_open_access_link', 'field_5ffca31d9e5f6'),
(1886, 1807, 'image', '1808'),
(1887, 1807, '_image', 'field_5ffcb3a32211f'),
(1888, 1807, 'caption', ''),
(1889, 1807, '_caption', 'field_5ffd5f2b99c98'),
(1890, 1807, 'abstract', '
Background
Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer’s disease (AD). Recent studies showed that ACD can also be reduced in the early stages.
Methods
We described different trends of evolution of ACD over 3 years in a cohort of memory-complainers and their association to amyloid burden and brain metabolism. We studied the impact of ACD at baseline on cognitive scores’ evolution and the association between longitudinal changes in ACD and in cognitive score.
Results
76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a steadily heightened ACD and were comparable to those with accurate ACD in terms of demographic characteristics and AD biomarkers. 4.25% constantly showed low ACD, had significantly higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution and no association between longitudinal changes in ACD and in cognitive scores.
Conclusions
ACD begins to decrease during the preclinical phase in a group of individuals, who are of great interest and need to be further characterized.'),
(1891, 1807, '_abstract', 'field_5ff869ab50ef3'),
(1892, 1807, 'description', 'With several drugs currently being tested, a very early diagnosis of Alzheimer\'s disease becomes even more important. But how to do that?
For years it has been thought that older adults who perceive a decline and complain about their memory should be monitored because they may be developing Alzheimer\'s dementia. But in fact, most older adults complain about their memory, as memory changes are normal with aging.
We have bucked the trend by using an advanced method of data analysis and discovering that:
Those who have complained about their memory (or language, attention...) for years, while their family/close friends do not notice any changes, are not at greater risk of progressing to Alzheimer\'s dementia. The purely subjective and lasting (over years) perception of memory loss could be linked to depression, anxiety, or other conditions.
Conversely, the situation in which family/friends notice a decline, albeit slight, that the individual systematically underestimates should orient the clinician towards suspected Alzheimer\'s disease
'),
(1893, 1807, '_description', 'field_5ff869cb50ef5'),
(1894, 1808, '_wp_attached_file', '2021/01/122096318_954861568341908_4699321636902992732_o.jpg'),
(1895, 1808, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:2048;s:6:"height";i:1152;s:4:"file";s:59:"2021/01/122096318_954861568341908_4699321636902992732_o.jpg";s:5:"sizes";a:5:{s:6:"medium";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-300x169.jpg";s:5:"width";i:300;s:6:"height";i:169;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:60:"122096318_954861568341908_4699321636902992732_o-1024x576.jpg";s:5:"width";i:1024;s:6:"height";i:576;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-768x432.jpg";s:5:"width";i:768;s:6:"height";i:432;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:60:"122096318_954861568341908_4699321636902992732_o-1536x864.jpg";s:5:"width";i:1536;s:6:"height";i:864;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1896, 1809, '_wp_attached_file', '2021/02/postdoc_EN-v1.pdf'),
(1897, 1809, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:21:"postdoc_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:29:"postdoc_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:30:"postdoc_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"postdoc_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(1898, 1809, '_edit_lock', '1612285609:1'),
(1899, 1811, '_wp_attached_file', '2021/02/clinica_image_analysis-v2.pdf'),
(1900, 1811, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:33:"clinica_image_analysis-v2-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:41:"clinica_image_analysis-v2-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:42:"clinica_image_analysis-v2-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:41:"clinica_image_analysis-v2-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(1901, 1811, '_edit_lock', '1612291530:1'),
(1902, 1813, '_edit_lock', '1612526526:11'),
(1903, 1813, '_edit_last', '11'),
(1904, 1813, 'title', 'The ethics of innovation for Alzheimer\'s disease: the risk of overstating evidence for metabolic enhancement protocols'),
(1905, 1813, '_title', 'field_5ff86849c7493'),
(1906, 1813, 'authors', ' Timothy Daly, Ignacio Mastroleo, David Gorski, Stéphane Epelbaum '),
(1907, 1813, '_authors', 'field_5ff868da50eec'),
(1908, 1813, 'date', '20210118'),
(1909, 1813, '_date', 'field_5ff868f750eed'),
(1910, 1813, 'journal', 'Theoretical Medicine and Bioethics (ISSN : 1386-7415, ESSN : 1573-1200)'),
(1911, 1813, '_journal', 'field_5ff8692750eee'),
(1912, 1813, 'keywords', 'a:7:{i:0;s:2:"34";i:1;s:2:"30";i:2;s:2:"36";i:3;s:2:"35";i:4;s:2:"37";i:5;s:2:"39";i:6;s:2:"38";}'),
(1913, 1813, '_keywords', 'field_5ff8695650eef'),
(1914, 1813, 'publisher_link', 'https://link.springer.com/article/10.1007/s11017-020-09536-7'),
(1915, 1813, '_publisher_link', 'field_5ff8696250ef0'),
(1916, 1813, 'open_access_link', 'https://hal.archives-ouvertes.fr/hal-03114575/document'),
(1917, 1813, '_open_access_link', 'field_5ffca31d9e5f6'),
(1918, 1813, 'image', '1814'),
(1919, 1813, '_image', 'field_5ffcb3a32211f'),
(1920, 1813, 'caption', 'Innovation and Alzheimer. Friend or foe ?'),
(1921, 1813, '_caption', 'field_5ffd5f2b99c98'),
(1922, 1813, 'abstract', 'Medical practice is ideally based on robust, relevant research. However, the lack of diseasemodifying treatments for Alzheimer\'s disease has motivated "innovative practice" to improve patients\' well-being despite insufficient evidence for the regular use of such interventions in health systems treating millions of patients. Innovative or new non-validated practice poses at least three distinct ethical questions: first, about the responsible application of new non-validated practice to individual patients (clinical ethics); second, about the way in which data from new non-validated practice are communicated via the scientific and lay press (scientific communication ethics); and third, about the prospect of making new non-validated interventions widely available before more definitive testing (public health ethics). We argue that the authors of metabolic enhancement protocols for Alzheimer\'s disease have overstated the evidence in favor of these interventions within the scientific and lay press, failing to communicate weaknesses in their data and uncertainty about their conclusions. Such unmeasured language may create false hope, cause financial harm, undermine informed consent, and frustrate the production of generalizable knowledge necessary to face the societal problems posed by this devastating disease. We therefore offer more stringent guidelines for responsible innovation in the treatment of Alzheimer\'s disease.'),
(1923, 1813, '_abstract', 'field_5ff869ab50ef3'),
(1924, 1813, 'description', 'In this article we describe the interplay between a chronic disease with severe therapeutic unmet need such as Alzheimer and innovation. Caveats and ethical guidelines are discussed herein.'),
(1925, 1813, '_description', 'field_5ff869cb50ef5'),
(1926, 1814, '_wp_attached_file', '2021/02/index.jpg'),
(1927, 1814, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:331;s:6:"height";i:152;s:4:"file";s:17:"2021/02/index.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:17:"index-300x138.jpg";s:5:"width";i:300;s:6:"height";i:138;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:17:"index-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1934, 1818, '_wp_attached_file', '2021/02/these_interpretability_EN-v1.pdf'),
(1935, 1818, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:36:"these_interpretability_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:44:"these_interpretability_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:45:"these_interpretability_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:44:"these_interpretability_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(1939, 1818, '_edit_lock', '1612947193:1'),
(1941, 1821, '_edit_lock', '1613549278:11'),
(1942, 1822, '_wp_attached_file', '2021/02/9783030597092.jpg'),
(1943, 1822, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597092.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597092-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1944, 1822, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I - Machine learning'),
(1945, 1821, '_edit_last', '11'),
(1946, 1821, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I - machine learning methodologies'),
(1947, 1821, '_title', 'field_5ff86849c7493'),
(1948, 1821, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'),
(1949, 1821, '_authors', 'field_5ff868da50eec'),
(1950, 1821, 'date', '20201004'),
(1951, 1821, '_date', 'field_5ff868f750eed'),
(1952, 1821, 'journal', 'Springer, LNCS 12261'),
(1953, 1821, '_journal', 'field_5ff8692750eee'),
(1954, 1821, 'keywords', 'a:2:{i:0;s:2:"26";i:1;s:2:"28";}'),
(1955, 1821, '_keywords', 'field_5ff8695650eef'),
(1956, 1821, 'publisher_link', 'https://www.springer.com/gp/book/9783030597092'),
(1957, 1821, '_publisher_link', 'field_5ff8696250ef0'),
(1958, 1821, 'open_access_link', ''),
(1959, 1821, '_open_access_link', 'field_5ffca31d9e5f6'),
(1960, 1821, 'image', '1822'),
(1961, 1821, '_image', 'field_5ffcb3a32211f'),
(1962, 1821, 'caption', 'MICCAI 2020 - Proceedings (Part I)'),
(1963, 1821, '_caption', 'field_5ffd5f2b99c98'),
(1964, 1821, 'abstract', 'The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.'),
(1965, 1821, '_abstract', 'field_5ff869ab50ef3'),
(1966, 1821, 'description', 'The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography'),
(1967, 1821, '_description', 'field_5ff869cb50ef5'),
(1968, 1824, '_edit_lock', '1613549743:11'),
(1969, 1824, '_edit_last', '11'),
(1970, 1825, '_wp_attached_file', '2021/02/9783030597122.jpg'),
(1971, 1825, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597122.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597122-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1972, 1825, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II'),
(1973, 1824, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II'),
(1974, 1824, '_title', 'field_5ff86849c7493'),
(1975, 1824, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'),
(1976, 1824, '_authors', 'field_5ff868da50eec'),
(1977, 1824, 'date', '20201004'),
(1978, 1824, '_date', 'field_5ff868f750eed'),
(1979, 1824, 'journal', 'Springer, LNCS 12262'),
(1980, 1824, '_journal', 'field_5ff8692750eee'),
(1981, 1824, 'keywords', 'a:2:{i:0;s:2:"28";i:1;s:2:"26";}'),
(1982, 1824, '_keywords', 'field_5ff8695650eef'),
(1983, 1824, 'publisher_link', 'https://www.springer.com/gp/book/9783030597122'),
(1984, 1824, '_publisher_link', 'field_5ff8696250ef0'),
(1985, 1824, 'open_access_link', ''),
(1986, 1824, '_open_access_link', 'field_5ffca31d9e5f6'),
(1987, 1824, 'image', '1825'),
(1988, 1824, '_image', 'field_5ffcb3a32211f'),
(1989, 1824, 'caption', 'MICCAI 2020 - Proceedings (Part II - image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks)'),
(1990, 1824, '_caption', 'field_5ffd5f2b99c98'),
(1991, 1824, 'abstract', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
'),
(1994, 1824, '_description', 'field_5ff869cb50ef5'),
(1995, 1826, '_edit_lock', '1613550093:11'),
(1996, 1827, '_wp_attached_file', '2021/02/9783030597153.jpg'),
(1997, 1827, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597153.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597153-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(1998, 1827, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III'),
(1999, 1826, '_edit_last', '11'),
(2000, 1826, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III'),
(2001, 1826, '_title', 'field_5ff86849c7493'),
(2002, 1826, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'),
(2003, 1826, '_authors', 'field_5ff868da50eec'),
(2004, 1826, 'date', '20201004'),
(2005, 1826, '_date', 'field_5ff868f750eed'),
(2006, 1826, 'journal', 'Springer, LNCS 12263'),
(2007, 1826, '_journal', 'field_5ff8692750eee'),
(2008, 1826, 'keywords', ''),
(2009, 1826, '_keywords', 'field_5ff8695650eef'),
(2010, 1826, 'publisher_link', 'https://www.springer.com/gp/book/9783030597153'),
(2011, 1826, '_publisher_link', 'field_5ff8696250ef0'),
(2012, 1826, 'open_access_link', ''),
(2013, 1826, '_open_access_link', 'field_5ffca31d9e5f6'),
(2014, 1826, 'image', '1827'),
(2015, 1826, '_image', 'field_5ffcb3a32211f'),
(2016, 1826, 'caption', 'MICCAI 2020 - Proceedings (Part III - CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis)'),
(2017, 1826, '_caption', 'field_5ffd5f2b99c98'),
(2018, 1826, 'abstract', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
'),
(2021, 1826, '_description', 'field_5ff869cb50ef5'),
(2022, 1828, '_edit_lock', '1616406335:11'),
(2023, 1828, '_edit_last', '11'),
(2026, 1830, '_wp_attached_file', '2021/02/these_IHI_EN-v1.pdf'),
(2027, 1830, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:23:"these_IHI_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:31:"these_IHI_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:32:"these_IHI_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:31:"these_IHI_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2028, 1831, '_wp_attached_file', '2021/02/these_MS_EN-v1.pdf'),
(2029, 1831, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:22:"these_MS_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:30:"these_MS_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:31:"these_MS_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:30:"these_MS_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2030, 1834, '_wp_attached_file', '2018/11/omarelrifai.jpg'),
(2031, 1834, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:774;s:6:"height";i:916;s:4:"file";s:23:"2018/11/omarelrifai.jpg";s:5:"sizes";a:3:{s:6:"medium";a:4:{s:4:"file";s:23:"omarelrifai-253x300.jpg";s:5:"width";i:253;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"omarelrifai-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:23:"omarelrifai-768x909.jpg";s:5:"width";i:768;s:6:"height";i:909;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:11:"KM_C308 Q76";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:11:"KM_C308 Q76";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(2032, 1835, '_edit_lock', '1618908373:8'),
(2033, 1835, '_edit_last', '11'),
(2038, 1835, 'title', 'BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks'),
(2039, 1835, '_title', 'field_5ff86849c7493'),
(2040, 1835, 'authors', 'Marie-Constance Corsi, Mario Chavez D.Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani'),
(2041, 1835, '_authors', 'field_5ff868da50eec'),
(2042, 1835, 'date', '20210316'),
(2043, 1835, '_date', 'field_5ff868f750eed'),
(2044, 1835, 'journal', 'Journal of Neural Engineering'),
(2045, 1835, '_journal', 'field_5ff8692750eee'),
(2046, 1835, 'keywords', 'a:5:{i:0;s:2:"40";i:1;s:2:"41";i:2;s:2:"42";i:3;s:2:"43";i:4;s:2:"44";}'),
(2047, 1835, '_keywords', 'field_5ff8695650eef'),
(2048, 1835, 'publisher_link', 'https://pubmed.ncbi.nlm.nih.gov/33725682/'),
(2049, 1835, '_publisher_link', 'field_5ff8696250ef0'),
(2050, 1835, 'open_access_link', 'https://hal.inria.fr/hal-03171591/'),
(2051, 1835, '_open_access_link', 'field_5ffca31d9e5f6'),
(2052, 1835, 'image', '1839'),
(2053, 1835, '_image', 'field_5ffcb3a32211f'),
(2054, 1835, 'caption', 'Behavioral performance and E/MEG contributions. (A) Distribution of BCI accuracy scores averaged across the runs of each session. Horizontal lines inside the box represent the median values. (B) Evolution of the E/MEG networks over sessions (average over the participants), obtained for each session, and condition within the alpha2 (top) and beta1 (bottom) ranges. (C) Evolution of attributed weights over sessions within the alpha2 (top) and beta1 (bottom) ranges. We plotted in grey and green the weight distribution associated, respectively, with EEG and MEG. Horizontal lines inside the box represent the median values.'),
(2055, 1835, '_caption', 'field_5ffd5f2b99c98'),
(2056, 1835, 'abstract', 'Objective
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is dicult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood.
Approach
To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time.
Main results
We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the alpha band was paralleled by a decrease of the integration of visual processing and working memory areas in the beta band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the alpha 2 band: positively in somatosensory and decision making related areas and negatively in associative areas.
Significance
Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.'),
(2057, 1835, '_abstract', 'field_5ff869ab50ef3'),
(2058, 1835, 'description', 'Reaching a high performance in controlling a brain-computer interface requires several sessions of training. Even though previous studies suggested the involvement of a distributed network, neural mechanisms underlying this learning process remains poorly understood. A recent work of our group led by J. Guillon proved that combining multimodal neuroimaging data from a network perspective can reveal properties that cannot be detected by approaches relying on a single modality.
In this study, we integrated multimodal brain network properties from electroencephalography (EEG) and magnetoencephalography (MEG), known to be complementary. More specifically, we studied coreness properties, defined as the probability for a given node to belong to a group of tightly connected nodes. We computed these properties both at the single modality level (EEG or MEG) and at the integrated level.
We observed similar trends in the evolution of the network properties over the BCI training sessions between single and multimodal levels. Notably, we obtained a significant association with the future BCI performance only in the case of the coreness properties resulting from the M/EEG integration. These findings suggest that this approach could give access to markers of BCI learning.'),
(2059, 1835, '_description', 'field_5ff869cb50ef5'),
(2062, 1839, '_wp_attached_file', '2021/03/Figure.png'),
(2063, 1839, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1737;s:6:"height";i:1125;s:4:"file";s:18:"2021/03/Figure.png";s:5:"sizes";a:5:{s:6:"medium";a:4:{s:4:"file";s:18:"Figure-300x194.png";s:5:"width";i:300;s:6:"height";i:194;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:19:"Figure-1024x663.png";s:5:"width";i:1024;s:6:"height";i:663;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:18:"Figure-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:18:"Figure-768x497.png";s:5:"width";i:768;s:6:"height";i:497;s:9:"mime-type";s:9:"image/png";}s:9:"1536x1536";a:4:{s:4:"file";s:19:"Figure-1536x995.png";s:5:"width";i:1536;s:6:"height";i:995;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2130, 1843, '_wp_attached_file', '2021/04/these_EDS.pdf'),
(2131, 1843, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:17:"these_EDS-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:25:"these_EDS-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:26:"these_EDS-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:25:"these_EDS-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2132, 1843, '_edit_lock', '1617868417:1'),
(2133, 1852, '_edit_lock', '1618908815:8'),
(2134, 1852, '_edit_last', '8'),
(2135, 1853, '_wp_attached_file', '2021/04/Capture-décran-2021-04-20-à-10.53.35.png'),
(2136, 1853, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1264;s:6:"height";i:736;s:4:"file";s:52:"2021/04/Capture-décran-2021-04-20-à-10.53.35.png";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:52:"Capture-décran-2021-04-20-à-10.53.35-300x175.png";s:5:"width";i:300;s:6:"height";i:175;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:53:"Capture-décran-2021-04-20-à-10.53.35-1024x596.png";s:5:"width";i:1024;s:6:"height";i:596;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:52:"Capture-décran-2021-04-20-à-10.53.35-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:52:"Capture-décran-2021-04-20-à-10.53.35-768x447.png";s:5:"width";i:768;s:6:"height";i:447;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2137, 1853, '_wp_attachment_image_alt', 'AD Course Map charts Alzheimer disease progression'),
(2138, 1852, 'title', 'AD Course Map charts Alzheimer\'s disease progression'),
(2139, 1852, '_title', 'field_5ff86849c7493'),
(2140, 1852, 'authors', 'Igor Koval, Alexandre Bône, Maxime Louis, Thomas Lartigue, Simona Bottani, Arnaud Marcoux, Jorge Samper-González, Ninon Burgos, Benjamin Charlier, Anne Bertrand, Stéphane Epelbaum, Olivier Colliot, Stéphanie Allassonnière & Stanley Durrleman'),
(2141, 1852, '_authors', 'field_5ff868da50eec'),
(2142, 1852, 'date', '20210413'),
(2143, 1852, '_date', 'field_5ff868f750eed'),
(2144, 1852, 'journal', 'Nature Scientific Reports'),
(2145, 1852, '_journal', 'field_5ff8692750eee'),
(2146, 1852, 'keywords', 'a:4:{i:0;s:2:"30";i:1;s:2:"45";i:2;s:2:"46";i:3;s:2:"47";}'),
(2147, 1852, '_keywords', 'field_5ff8695650eef'),
(2148, 1852, 'publisher_link', 'https://www.nature.com/articles/s41598-021-87434-1#Sec1'),
(2149, 1852, '_publisher_link', 'field_5ff8696250ef0'),
(2150, 1852, 'open_access_link', 'https://www.nature.com/articles/s41598-021-87434-1#Sec1'),
(2151, 1852, '_open_access_link', 'field_5ffca31d9e5f6'),
(2152, 1852, 'image', '1853'),
(2153, 1852, '_image', 'field_5ffcb3a32211f'),
(2154, 1852, 'caption', 'Normative models of Alzheimer’s disease progression shown at 4 Alzheimer Age with estimated time until/from diagnosis. Bottom to top rows show alteration of brain glucose metabolism, hippocampus atrophy, cortical thinning and onset of cognitive decline. Black arrows and ellipses indicate some areas of great changes'),
(2155, 1852, '_caption', 'field_5ffd5f2b99c98'),
(2156, 1852, 'abstract', 'Alzheimer’s disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer’s disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient’s cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer’s disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.'),
(2157, 1852, '_abstract', 'field_5ff869ab50ef3'),
(2158, 1852, 'description', '
In this paper, we have been able to simultaneously characterize the progression of cognitive assessments, the cortical thickness, meshes of the hippocampus and glucose consumption measured with PET-FDG over a period of 30 years during the course of Alzheimer’s disease.
Such a description allows to precisely quantify the influence of different cofactors on the disease progression. But more importantly, this description of unprecedented precision allows to position any individual on the disease timeline in order to forecast the values of his or her modalities up to 4 years ahead.
'),
(2159, 1852, '_description', 'field_5ff869cb50ef5'),
(2160, 1751, '_oembed_0967cfc78c889e99c4b973c13d56927f', '{{unknown}}'),
(2161, 1856, '_wp_attached_file', '2018/11/sophie_loizillon.jpeg'),
(2162, 1856, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:574;s:6:"height";i:698;s:4:"file";s:29:"2018/11/sophie_loizillon.jpeg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:29:"sophie_loizillon-247x300.jpeg";s:5:"width";i:247;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"sophie_loizillon-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2163, 1857, '_wp_attached_file', '2018/11/guanghui_fu.jpg'),
(2164, 1857, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:528;s:6:"height";i:529;s:4:"file";s:23:"2018/11/guanghui_fu.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:23:"guanghui_fu-300x300.jpg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"guanghui_fu-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2165, 1858, '_wp_attached_file', '2018/11/nemo_fournier.jpg'),
(2166, 1858, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:615;s:6:"height";i:590;s:4:"file";s:25:"2018/11/nemo_fournier.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:25:"nemo_fournier-300x288.jpg";s:5:"width";i:300;s:6:"height";i:288;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:25:"nemo_fournier-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2167, 1859, '_wp_attached_file', '2018/11/Mehdi_OUNISSI-scaled.jpg'),
(2168, 1859, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1916;s:6:"height";i:2560;s:4:"file";s:32:"2018/11/Mehdi_OUNISSI-scaled.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:25:"Mehdi_OUNISSI-225x300.jpg";s:5:"width";i:225;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:26:"Mehdi_OUNISSI-767x1024.jpg";s:5:"width";i:767;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:25:"Mehdi_OUNISSI-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:26:"Mehdi_OUNISSI-768x1026.jpg";s:5:"width";i:768;s:6:"height";i:1026;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:27:"Mehdi_OUNISSI-1150x1536.jpg";s:5:"width";i:1150;s:6:"height";i:1536;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:27:"Mehdi_OUNISSI-1533x2048.jpg";s:5:"width";i:1533;s:6:"height";i:2048;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}s:14:"original_image";s:17:"Mehdi_OUNISSI.jpg";}'),
(2169, 1860, '_wp_attached_file', '2018/11/lisa_hemforth.jpeg'),
(2170, 1860, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:500;s:6:"height";i:529;s:4:"file";s:26:"2018/11/lisa_hemforth.jpeg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:26:"lisa_hemforth-284x300.jpeg";s:5:"width";i:284;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:26:"lisa_hemforth-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2171, 1862, '_wp_attached_file', '2018/11/thibault_rolland.jpg'),
(2172, 1862, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:512;s:6:"height";i:644;s:4:"file";s:28:"2018/11/thibault_rolland.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:28:"thibault_rolland-239x300.jpg";s:5:"width";i:239;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:28:"thibault_rolland-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2173, 1863, '_wp_attached_file', '2018/11/Anuradha_Kar.jpeg'),
(2174, 1863, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1018;s:6:"height";i:1297;s:4:"file";s:25:"2018/11/Anuradha_Kar.jpeg";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:25:"Anuradha_Kar-235x300.jpeg";s:5:"width";i:235;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:26:"Anuradha_Kar-804x1024.jpeg";s:5:"width";i:804;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:25:"Anuradha_Kar-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:25:"Anuradha_Kar-768x978.jpeg";s:5:"width";i:768;s:6:"height";i:978;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2175, 1864, '_wp_attached_file', '2018/11/juli_photo_equipe.jpg'),
(2176, 1864, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:291;s:6:"height";i:399;s:4:"file";s:29:"2018/11/juli_photo_equipe.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:29:"juli_photo_equipe-219x300.jpg";s:5:"width";i:219;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"juli_photo_equipe-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2177, 1865, '_wp_attached_file', '2018/11/matthieu_joulot-scaled.jpeg'),
(2178, 1865, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1440;s:6:"height";i:2560;s:4:"file";s:35:"2018/11/matthieu_joulot-scaled.jpeg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:28:"matthieu_joulot-169x300.jpeg";s:5:"width";i:169;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:29:"matthieu_joulot-576x1024.jpeg";s:5:"width";i:576;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:28:"matthieu_joulot-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:29:"matthieu_joulot-768x1365.jpeg";s:5:"width";i:768;s:6:"height";i:1365;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:29:"matthieu_joulot-864x1536.jpeg";s:5:"width";i:864;s:6:"height";i:1536;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:30:"matthieu_joulot-1152x2048.jpeg";s:5:"width";i:1152;s:6:"height";i:2048;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}s:14:"original_image";s:20:"matthieu_joulot.jpeg";}'),
(2179, 1866, '_wp_attached_file', '2021/11/OpenViBE-optimization_Internship.pdf'),
(2180, 1866, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:40:"OpenViBE-optimization_Internship-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:48:"OpenViBE-optimization_Internship-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:49:"OpenViBE-optimization_Internship-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:48:"OpenViBE-optimization_Internship-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2181, 1868, '_wp_attached_file', '2021/11/MasterProject_StratifIAD_2022.pdf'),
(2182, 1868, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:37:"MasterProject_StratifIAD_2022-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:45:"MasterProject_StratifIAD_2022-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:46:"MasterProject_StratifIAD_2022-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:45:"MasterProject_StratifIAD_2022-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2184, 1873, '_wp_attached_file', '2018/11/profile.png'),
(2185, 1873, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:286;s:6:"height";i:340;s:4:"file";s:19:"2018/11/profile.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:19:"profile-252x300.png";s:5:"width";i:252;s:6:"height";i:300;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:19:"profile-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2186, 1877, '_wp_attached_file', '2018/11/rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66.jpg'),
(2187, 1877, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:2234;s:6:"height";i:2145;s:4:"file";s:73:"2018/11/rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:73:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-300x288.jpg";s:5:"width";i:300;s:6:"height";i:288;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:74:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-1024x983.jpg";s:5:"width";i:1024;s:6:"height";i:983;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:73:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:73:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-768x737.jpg";s:5:"width";i:768;s:6:"height";i:737;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:75:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-1536x1475.jpg";s:5:"width";i:1536;s:6:"height";i:1475;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:75:"rn_image_picker_lib_temp_52508e8c-1fd7-47fa-9ce3-425cbd472c66-2048x1966.jpg";s:5:"width";i:2048;s:6:"height";i:1966;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:3:"2.2";s:6:"credit";s:0:"";s:6:"camera";s:8:"SM-A515F";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1641807270";s:9:"copyright";s:0:"";s:12:"focal_length";s:4:"3.72";s:3:"iso";s:3:"400";s:13:"shutter_speed";s:16:"0.03030303030303";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(2188, 1878, '_wp_attached_file', '2018/11/Baptiste_Couvy-Duchesne_2020.jpg'),
(2189, 1878, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:325;s:6:"height";i:443;s:4:"file";s:40:"2018/11/Baptiste_Couvy-Duchesne_2020.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:40:"Baptiste_Couvy-Duchesne_2020-220x300.jpg";s:5:"width";i:220;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:40:"Baptiste_Couvy-Duchesne_2020-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2191, 1882, '_wp_attached_file', '2018/11/photo_arya.jpeg'),
(2192, 1882, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1444;s:6:"height";i:1477;s:4:"file";s:23:"2018/11/photo_arya.jpeg";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:23:"photo_arya-293x300.jpeg";s:5:"width";i:293;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:25:"photo_arya-1001x1024.jpeg";s:5:"width";i:1001;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"photo_arya-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:23:"photo_arya-768x786.jpeg";s:5:"width";i:768;s:6:"height";i:786;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2193, 1885, '_wp_attached_file', '2022/02/clinicadl_logo.png'),
(2194, 1885, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:400;s:6:"height";i:385;s:4:"file";s:26:"2022/02/clinicadl_logo.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:26:"clinicadl_logo-300x289.png";s:5:"width";i:300;s:6:"height";i:289;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:26:"clinicadl_logo-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2195, 1886, '_wp_attached_file', '2022/02/version_pieuvre_noire.png'),
(2196, 1886, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:400;s:6:"height";i:385;s:4:"file";s:33:"2022/02/version_pieuvre_noire.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:33:"version_pieuvre_noire-300x289.png";s:5:"width";i:300;s:6:"height";i:289;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:33:"version_pieuvre_noire-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2197, 1887, '_wp_attached_file', '2022/02/2021_Thibeau-Sutre_ClinicaDL_manuscript.pdf'),
(2198, 1887, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:47:"2021_Thibeau-Sutre_ClinicaDL_manuscript-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:55:"2021_Thibeau-Sutre_ClinicaDL_manuscript-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:56:"2021_Thibeau-Sutre_ClinicaDL_manuscript-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:55:"2021_Thibeau-Sutre_ClinicaDL_manuscript-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2199, 1890, '_wp_attached_file', '2022/02/leaspy_logo.png'),
(2200, 1890, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:506;s:6:"height";i:303;s:4:"file";s:23:"2022/02/leaspy_logo.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:23:"leaspy_logo-300x180.png";s:5:"width";i:300;s:6:"height";i:180;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"leaspy_logo-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2201, 1907, '_wp_attached_file', '2022/02/Lab-Manager.pdf'),
(2202, 1907, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:19:"Lab-Manager-pdf.jpg";s:5:"width";i:1060;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:27:"Lab-Manager-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"Lab-Manager-pdf-725x1024.jpg";s:5:"width";i:725;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:27:"Lab-Manager-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2203, 1907, '_edit_lock', '1645639246:1'),
(2204, 1913, '_wp_attached_file', '2022/02/clinicaDL_job_offer_v1.pdf'),
(2205, 1913, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:30:"clinicaDL_job_offer_v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:38:"clinicaDL_job_offer_v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:39:"clinicaDL_job_offer_v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:38:"clinicaDL_job_offer_v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2206, 1916, '_edit_lock', '1651211786:11'),
(2207, 1916, '_edit_last', '11'),
(2208, 1917, '_wp_attached_file', '2022/04/adni.png'),
(2209, 1917, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:2539;s:6:"height";i:840;s:4:"file";s:16:"2022/04/adni.png";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:15:"adni-300x99.png";s:5:"width";i:300;s:6:"height";i:99;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:17:"adni-1024x339.png";s:5:"width";i:1024;s:6:"height";i:339;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:16:"adni-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:16:"adni-768x254.png";s:5:"width";i:768;s:6:"height";i:254;s:9:"mime-type";s:9:"image/png";}s:9:"1536x1536";a:4:{s:4:"file";s:17:"adni-1536x508.png";s:5:"width";i:1536;s:6:"height";i:508;s:9:"mime-type";s:9:"image/png";}s:9:"2048x2048";a:4:{s:4:"file";s:17:"adni-2048x678.png";s:5:"width";i:2048;s:6:"height";i:678;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2210, 1916, 'title', 'Riemannian metric learning for progression modeling of longitudinal datasets'),
(2211, 1916, '_title', 'field_5ff86849c7493'),
(2212, 1916, 'authors', 'Benoît Sauty, Stanley Durrleman'),
(2213, 1916, '_authors', 'field_5ff868da50eec'),
(2214, 1916, 'date', '20220328'),
(2215, 1916, '_date', 'field_5ff868f750eed'),
(2216, 1916, 'journal', 'International Symposium on Biomedical Imaging (ISBI) 2022'),
(2217, 1916, '_journal', 'field_5ff8692750eee'),
(2218, 1916, 'keywords', 'a:3:{i:0;s:2:"45";i:1;s:2:"30";i:2;s:2:"46";}'),
(2219, 1916, '_keywords', 'field_5ff8695650eef'),
(2220, 1916, 'publisher_link', ''),
(2221, 1916, '_publisher_link', 'field_5ff8696250ef0'),
(2222, 1916, 'open_access_link', 'https://hal.inria.fr/hal-03549061'),
(2223, 1916, '_open_access_link', 'field_5ffca31d9e5f6'),
(2224, 1916, 'image', '1917'),
(2225, 1916, '_image', 'field_5ffcb3a32211f'),
(2226, 1916, 'caption', 'Normative scenario predicted by our model for 3 biomarkers : main logistic (plain) and parallels (dotted). One parrallel curve is highlighted with crosses.'),
(2227, 1916, '_caption', 'field_5ffd5f2b99c98'),
(2228, 1916, 'abstract', 'Explicit descriptions of the progression of biomarkers across time usually involve priors on the shapes of the trajectories. To circumvent this limitation, we propose a geometric frame- work to learn a manifold representation of longitudinal data. Namely, we introduce a family of Riemannian metrics that span a set of curves defined as parallel variations around a main geodesic, and apply that framework to disease progression modeling with a mixed-effects model, where the main geodesic represents the average progression of biomarkers and parallel curves describe the individual trajectories. Learning the metric from the data allows to fit the model to longitudinal datasets and provides few interpretable parameters that characterize both the group-average trajectory and individual progression profiles. Our method outperforms the 56 methods benchmarked in the TADPOLE challenge for cognitive scores prediction.'),
(2229, 1916, '_abstract', 'field_5ff869ab50ef3'),
(2230, 1916, 'description', 'In this work we propose new progression models for biomarkers across Alzheimer\'s disease. For any biomarker, brain volumes or cognitive scores for instance, we learn the shape of the average trajectory of decline, as well individual progression profiles that describe each individual patient\'s trajectory. Most importantly, we know if each patients starts declining earlier or later than average, and declines faster or slower than average.
This allows to both predict future evolution of patients with higher certainty than former methods, and also describe the severity of the decline compared to an average scenario that is truly representative of the underlying biological processes. This method is applied to biomarkers for Alzheimer\'s Disease patients.'),
(2231, 1916, '_description', 'field_5ff869cb50ef5'),
(2232, 1919, '_wp_attached_file', '2018/11/portrait-nico-8.jpg'),
(2233, 1919, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:2137;s:6:"height";i:2057;s:4:"file";s:27:"2018/11/portrait-nico-8.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:27:"portrait-nico-8-300x289.jpg";s:5:"width";i:300;s:6:"height";i:289;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"portrait-nico-8-1024x986.jpg";s:5:"width";i:1024;s:6:"height";i:986;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:27:"portrait-nico-8-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"portrait-nico-8-768x739.jpg";s:5:"width";i:768;s:6:"height";i:739;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:29:"portrait-nico-8-1536x1478.jpg";s:5:"width";i:1536;s:6:"height";i:1478;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:29:"portrait-nico-8-2048x1971.jpg";s:5:"width";i:2048;s:6:"height";i:1971;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"8";s:6:"credit";s:15:"Isabelle Zezima";s:6:"camera";s:8:"NIKON D4";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1463414161";s:9:"copyright";s:15:"Isabelle Zezima";s:12:"focal_length";s:2:"70";s:3:"iso";s:3:"100";s:13:"shutter_speed";s:7:"0.00625";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(2236, 1921, '_wp_attached_file', '2018/11/WIN_20220420_11_51_11_Pro-2-1.jpg'),
(2237, 1921, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:639;s:6:"height";i:789;s:4:"file";s:41:"2018/11/WIN_20220420_11_51_11_Pro-2-1.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:41:"WIN_20220420_11_51_11_Pro-2-1-243x300.jpg";s:5:"width";i:243;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:41:"WIN_20220420_11_51_11_Pro-2-1-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1650455471";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(2238, 1674, '_edit_lock', '1670246515:12'),
(2247, 1926, '_wp_attached_file', '2018/11/mattermost_IMG_2732_crop3-e1653299687134.jpg'),
(2248, 1926, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:868;s:6:"height";i:929;s:4:"file";s:52:"2018/11/mattermost_IMG_2732_crop3-e1653299687134.jpg";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:52:"mattermost_IMG_2732_crop3-e1653299687134-280x300.jpg";s:5:"width";i:280;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:38:"mattermost_IMG_2732_crop3-1024x884.jpg";s:5:"width";i:1024;s:6:"height";i:884;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:52:"mattermost_IMG_2732_crop3-e1653299687134-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:52:"mattermost_IMG_2732_crop3-e1653299687134-768x822.jpg";s:5:"width";i:768;s:6:"height";i:822;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:3:"2.8";s:6:"credit";s:0:"";s:6:"camera";s:19:"Canon PowerShot G11";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1601300078";s:9:"copyright";s:0:"";s:12:"focal_length";s:3:"6.1";s:3:"iso";s:3:"640";s:13:"shutter_speed";s:17:"0.033333333333333";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'),
(2249, 1926, '_wp_attachment_backup_sizes', 'a:5:{s:9:"full-orig";a:3:{s:5:"width";i:1277;s:6:"height";i:1102;s:4:"file";s:29:"mattermost_IMG_2732_crop3.jpg";}s:14:"thumbnail-orig";a:4:{s:4:"file";s:37:"mattermost_IMG_2732_crop3-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:11:"medium-orig";a:4:{s:4:"file";s:37:"mattermost_IMG_2732_crop3-300x259.jpg";s:5:"width";i:300;s:6:"height";i:259;s:9:"mime-type";s:10:"image/jpeg";}s:17:"medium_large-orig";a:4:{s:4:"file";s:37:"mattermost_IMG_2732_crop3-768x663.jpg";s:5:"width";i:768;s:6:"height";i:663;s:9:"mime-type";s:10:"image/jpeg";}s:10:"large-orig";a:4:{s:4:"file";s:38:"mattermost_IMG_2732_crop3-1024x884.jpg";s:5:"width";i:1024;s:6:"height";i:884;s:9:"mime-type";s:10:"image/jpeg";}}'),
(2254, 1930, '_wp_attached_file', '2022/05/Technicien-Fr.pdf'),
(2255, 1930, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:21:"Technicien-Fr-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:29:"Technicien-Fr-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:30:"Technicien-Fr-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"Technicien-Fr-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2256, 1933, '_wp_attached_file', '2018/11/Capture-décran-2022-06-14-à-14.55.50.png'),
(2257, 1933, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:324;s:6:"height";i:330;s:4:"file";s:52:"2018/11/Capture-décran-2022-06-14-à-14.55.50.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:52:"Capture-décran-2022-06-14-à-14.55.50-295x300.png";s:5:"width";i:295;s:6:"height";i:300;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:52:"Capture-décran-2022-06-14-à-14.55.50-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2258, 1936, '_edit_lock', '1659365662:11'),
(2259, 1936, '_edit_last', '11'),
(2260, 1937, '_wp_attached_file', '2022/08/adni_mri.png'),
(2261, 1937, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1642;s:6:"height";i:788;s:4:"file";s:20:"2022/08/adni_mri.png";s:5:"sizes";a:5:{s:6:"medium";a:4:{s:4:"file";s:20:"adni_mri-300x144.png";s:5:"width";i:300;s:6:"height";i:144;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:21:"adni_mri-1024x491.png";s:5:"width";i:1024;s:6:"height";i:491;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:20:"adni_mri-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:20:"adni_mri-768x369.png";s:5:"width";i:768;s:6:"height";i:369;s:9:"mime-type";s:9:"image/png";}s:9:"1536x1536";a:4:{s:4:"file";s:21:"adni_mri-1536x737.png";s:5:"width";i:1536;s:6:"height";i:737;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2262, 1936, 'title', 'Progression models for imaging data with Longitudinal Variational Auto Encoders'),
(2263, 1936, '_title', 'field_5ff86849c7493'),
(2264, 1936, 'authors', 'Benoît Sauty, Stanley Durrleman'),
(2265, 1936, '_authors', 'field_5ff868da50eec'),
(2266, 1936, 'date', '20220919'),
(2267, 1936, '_date', 'field_5ff868f750eed'),
(2268, 1936, 'journal', 'International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022'),
(2269, 1936, '_journal', 'field_5ff8692750eee'),
(2270, 1936, 'keywords', 'a:2:{i:0;s:2:"45";i:1;s:2:"28";}'),
(2271, 1936, '_keywords', 'field_5ff8695650eef'),
(2272, 1936, 'publisher_link', ''),
(2273, 1936, '_publisher_link', 'field_5ff8696250ef0'),
(2274, 1936, 'open_access_link', 'https://hal.inria.fr/INRIA/hal-03701632'),
(2275, 1936, '_open_access_link', 'field_5ffca31d9e5f6'),
(2276, 1936, 'image', '1937'),
(2277, 1936, '_image', 'field_5ffcb3a32211f'),
(2278, 1936, 'caption', ''),
(2279, 1936, '_caption', 'field_5ffd5f2b99c98'),
(2280, 1936, 'abstract', 'Disease progression models are crucial to understanding degenerative diseases. Mixed-effects models have been consistently used to model clinical assessments or biomarkers extracted from medical images, allowing missing data imputation and prediction at any timepoint. However, such progression models have seldom been used for entire medical images. In this work, a Variational Auto Encoder is coupled with a temporal linear mixed-effect model to learn a latent representation of the data such that individual trajectories follow straight lines over time and are characterised by a few interpretable parameters. A Monte Carlo estimator is devised to iteratively optimize the networks and the statistical model. We apply this method on a synthetic data set to illustrate the disentanglement between time dependant changes and inter-subjects variability, as well as the predictive capabilities of the method. We then apply it to 3D MRI and FDG-PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to recover well documented patterns of structural and metabolic alterations of the brain.'),
(2281, 1936, '_abstract', 'field_5ff869ab50ef3'),
(2282, 1936, 'description', 'This work presents a framework to predict how the brain will look in the future from imaging scans at a given time. This is very important as it allows us to predict how patients with neurodegenerative diseases will evolve before the disease really kicks in !'),
(2283, 1936, '_description', 'field_5ff869cb50ef5'),
(2284, 1939, '_wp_attached_file', '2018/11/Nadine-Hamieh-Small.jpeg'),
(2285, 1939, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:255;s:6:"height";i:320;s:4:"file";s:32:"2018/11/Nadine-Hamieh-Small.jpeg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:32:"Nadine-Hamieh-Small-239x300.jpeg";s:5:"width";i:239;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:32:"Nadine-Hamieh-Small-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2289, 1945, '_wp_attached_file', '2022/10/clinica_brain_image_analysis.pdf'),
(2290, 1945, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:36:"clinica_brain_image_analysis-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:44:"clinica_brain_image_analysis-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:45:"clinica_brain_image_analysis-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:44:"clinica_brain_image_analysis-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2295, 1951, '_wp_attached_file', '2018/11/Photo-Lydia.jpg'),
(2296, 1951, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:588;s:6:"height";i:461;s:4:"file";s:23:"2018/11/Photo-Lydia.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:23:"Photo-Lydia-300x235.jpg";s:5:"width";i:300;s:6:"height";i:235;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"Photo-Lydia-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2297, 1952, '_wp_attached_file', '2018/11/photo_ghislain.jpeg'),
(2298, 1952, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:200;s:6:"height";i:200;s:4:"file";s:27:"2018/11/photo_ghislain.jpeg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:27:"photo_ghislain-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2299, 1953, '_wp_attached_file', '2018/11/Photo_octave.png'),
(2300, 1953, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:674;s:6:"height";i:634;s:4:"file";s:24:"2018/11/Photo_octave.png";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:24:"Photo_octave-300x282.png";s:5:"width";i:300;s:6:"height";i:282;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:24:"Photo_octave-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2301, 1954, '_wp_attached_file', '2018/11/Photo_Sofia-scaled.jpg'),
(2302, 1954, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:2560;s:6:"height";i:2138;s:4:"file";s:30:"2018/11/Photo_Sofia-scaled.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:23:"Photo_Sofia-300x251.jpg";s:5:"width";i:300;s:6:"height";i:251;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:24:"Photo_Sofia-1024x855.jpg";s:5:"width";i:1024;s:6:"height";i:855;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"Photo_Sofia-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:23:"Photo_Sofia-768x641.jpg";s:5:"width";i:768;s:6:"height";i:641;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:25:"Photo_Sofia-1536x1283.jpg";s:5:"width";i:1536;s:6:"height";i:1283;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:25:"Photo_Sofia-2048x1711.jpg";s:5:"width";i:2048;s:6:"height";i:1711;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:3:"3.4";s:6:"credit";s:0:"";s:6:"camera";s:13:"COOLPIX A1000";s:7:"caption";s:0:"";s:17:"created_timestamp";s:10:"1587164669";s:9:"copyright";s:0:"";s:12:"focal_length";s:3:"4.3";s:3:"iso";s:3:"400";s:13:"shutter_speed";s:4:"0.05";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}s:14:"original_image";s:15:"Photo_Sofia.jpg";}'),
(2303, 1958, '_wp_attached_file', '2022/11/MasterProject_StratifIAD_2023.pdf'),
(2304, 1958, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:37:"MasterProject_StratifIAD_2023-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:45:"MasterProject_StratifIAD_2023-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:46:"MasterProject_StratifIAD_2023-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:45:"MasterProject_StratifIAD_2023-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2305, 1960, '_wp_attached_file', '2022/11/2023_master-internship-offer_Crohn-AI.pdf'),
(2306, 1960, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:45:"2023_master-internship-offer_Crohn-AI-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:53:"2023_master-internship-offer_Crohn-AI-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:54:"2023_master-internship-offer_Crohn-AI-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:53:"2023_master-internship-offer_Crohn-AI-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2307, 30, '_oembed_181b35d72dfbb99790b4eed45401c46b', '{{unknown}}'),
(2308, 1964, '_wp_attached_file', '2022/11/Master_intern_MALMO.pdf'),
(2309, 1964, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:27:"Master_intern_MALMO-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:35:"Master_intern_MALMO-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:36:"Master_intern_MALMO-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:35:"Master_intern_MALMO-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'),
(2310, 1969, '_edit_lock', '1736953679:9'),
(2311, 1969, '_edit_last', '12'),
(2312, 1969, '_wp_page_template', 'default'),
(2322, 1969, '_subtitle', 'APPRIMAGE PROJECT'),
(2325, 1856, '_edit_lock', '1673522506:1'),
(2326, 1980, '_wp_attached_file', '2018/11/camille_brianceau.png'),
(2327, 1980, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1200;s:6:"height";i:1600;s:4:"file";s:29:"2018/11/camille_brianceau.png";s:8:"filesize";i:1942610;s:5:"sizes";a:5:{s:6:"medium";a:5:{s:4:"file";s:29:"camille_brianceau-225x300.png";s:5:"width";i:225;s:6:"height";i:300;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:85642;}s:5:"large";a:5:{s:4:"file";s:30:"camille_brianceau-768x1024.png";s:5:"width";i:768;s:6:"height";i:1024;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:792999;}s:9:"thumbnail";a:5:{s:4:"file";s:29:"camille_brianceau-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:39977;}s:12:"medium_large";a:5:{s:4:"file";s:30:"camille_brianceau-768x1024.png";s:5:"width";i:768;s:6:"height";i:1024;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:792999;}s:9:"1536x1536";a:5:{s:4:"file";s:31:"camille_brianceau-1152x1536.png";s:5:"width";i:1152;s:6:"height";i:1536;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:1596937;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2328, 1981, '_wp_attached_file', '2018/11/rosana.jpg'),
(2329, 1981, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:198;s:6:"height";i:276;s:4:"file";s:18:"2018/11/rosana.jpg";s:8:"filesize";i:18281;s:5:"sizes";a:1:{s:9:"thumbnail";a:5:{s:4:"file";s:18:"rosana-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:5514;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2330, 1283, '_edit_lock', '1678611859:1'),
(2332, 1987, '_edit_lock', '1680874413:12'),
(2333, 1987, '_edit_last', '12'),
(2334, 1988, '_wp_attached_file', '2023/04/41467_2022_35712_Fig4_HTML.png'),
(2335, 1988, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:2002;s:6:"height";i:963;s:4:"file";s:38:"2023/04/41467_2022_35712_Fig4_HTML.png";s:8:"filesize";i:178795;s:5:"sizes";a:5:{s:6:"medium";a:5:{s:4:"file";s:38:"41467_2022_35712_Fig4_HTML-300x144.png";s:5:"width";i:300;s:6:"height";i:144;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:25366;}s:5:"large";a:5:{s:4:"file";s:39:"41467_2022_35712_Fig4_HTML-1024x493.png";s:5:"width";i:1024;s:6:"height";i:493;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:148461;}s:9:"thumbnail";a:5:{s:4:"file";s:38:"41467_2022_35712_Fig4_HTML-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:14618;}s:12:"medium_large";a:5:{s:4:"file";s:38:"41467_2022_35712_Fig4_HTML-768x369.png";s:5:"width";i:768;s:6:"height";i:369;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:99579;}s:9:"1536x1536";a:5:{s:4:"file";s:39:"41467_2022_35712_Fig4_HTML-1536x739.png";s:5:"width";i:1536;s:6:"height";i:739;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:252757;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2336, 1987, 'title', 'Forecasting individual progression trajectories in Alzheimer\'s disease'),
(2337, 1987, '_title', 'field_5ff86849c7493'),
(2338, 1987, 'authors', 'Etienne Maheux, Igor Koval, Juliette Ortholand, Colin Birkenbihl, Damiano Archetti, Vincent Bouteloup, Stéphane Epelbaum, Carole Dufouil, Martin Hofmann-Apitius & Stanley Durrleman'),
(2339, 1987, '_authors', 'field_5ff868da50eec'),
(2340, 1987, 'date', '20230210'),
(2341, 1987, '_date', 'field_5ff868f750eed'),
(2342, 1987, 'journal', 'Nature Communications (volume 14)'),
(2343, 1987, '_journal', 'field_5ff8692750eee'),
(2344, 1987, 'keywords', 'a:4:{i:0;s:2:"45";i:1;s:2:"30";i:2;s:2:"48";i:3;s:2:"46";}'),
(2345, 1987, '_keywords', 'field_5ff8695650eef'),
(2346, 1987, 'publisher_link', 'https://www.nature.com/articles/s41467-022-35712-5'),
(2347, 1987, '_publisher_link', 'field_5ff8696250ef0'),
(2348, 1987, 'open_access_link', 'https://www.nature.com/articles/s41467-022-35712-5'),
(2349, 1987, '_open_access_link', 'field_5ffca31d9e5f6'),
(2350, 1987, 'image', '1988'),
(2351, 1987, '_image', 'field_5ffcb3a32211f'),
(2352, 1987, 'caption', 'Trial participants are selected first using standard inclusion criteria and undergo a series of exams. A disease progression model, such as AD Course Map, then forecasts the progression of each participant\'s data and predicts if the participant is likely to progress significantly during the trial, as measured by the predicted outcome change, which is the mini-mental state examination (MMSE) in this example. The treatment effect (e.g., a 25% reduction of the change of the MMSE during trial) leads to a greater effect size, and therefore a smaller sample size, on the group of predicted fast progressors compared to the group of predicted slow progressors or the two groups combined. As a result, one may demonstrate the treatment efficacy with fewer participants by monitoring only the group of predicted fast progressors.'),
(2353, 1987, '_caption', 'field_5ffd5f2b99c98'),
(2354, 1987, 'abstract', 'The anticipation of progression of Alzheimer\'s disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.'),
(2355, 1987, '_abstract', 'field_5ff869ab50ef3'),
(2356, 1987, 'description', 'This work is two-fold: it first evaluates performance, generalization and fairness of single-subject predictions during the course of Alzheimer\'s disease, in a pool of 4,600+ individuals from 5 independent cohorts, using different predictive models including the AD Course Map, a versatile and interpretable disease progression model, and a state-of-the-art recurrent neural network. The use of these single-subject predictions in a prognostic enrichment strategy is then validated in 6 simulated clinical trials mimicking recent or on-going AD trials. Authors show that enriching the population with the likely decliners decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD.'),
(2357, 1987, '_description', 'field_5ff869cb50ef5'),
(2358, 1998, '_wp_attached_file', '2023/12/clinicaDL_job_offer_v1.0.pdf'),
(2359, 1998, '_wp_attachment_metadata', 'a:2:{s:5:"sizes";a:4:{s:4:"full";a:5:{s:4:"file";s:32:"clinicaDL_job_offer_v1.0-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:88206;}s:6:"medium";a:5:{s:4:"file";s:40:"clinicaDL_job_offer_v1.0-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:8684;}s:5:"large";a:5:{s:4:"file";s:41:"clinicaDL_job_offer_v1.0-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:44595;}s:9:"thumbnail";a:5:{s:4:"file";s:40:"clinicaDL_job_offer_v1.0-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:5156;}}s:8:"filesize";i:305491;}'),
(2360, 1999, '_wp_attached_file', '2023/12/2023_Clinica.pdf'),
(2361, 1999, '_wp_attachment_metadata', 'a:2:{s:5:"sizes";a:4:{s:4:"full";a:5:{s:4:"file";s:20:"2023_Clinica-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:70038;}s:6:"medium";a:5:{s:4:"file";s:28:"2023_Clinica-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:7561;}s:5:"large";a:5:{s:4:"file";s:29:"2023_Clinica-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:35603;}s:9:"thumbnail";a:5:{s:4:"file";s:28:"2023_Clinica-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:4613;}}s:8:"filesize";i:298843;}'),
(2362, 2000, '_wp_attached_file', '2023/12/Automated_MRI_Quality_Assessment_CDW_Artefact_Simulation_FLAIR.pdf'),
(2363, 2000, '_wp_attachment_metadata', 'a:2:{s:5:"sizes";a:4:{s:4:"full";a:5:{s:4:"file";s:70:"Automated_MRI_Quality_Assessment_CDW_Artefact_Simulation_FLAIR-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:76361;}s:6:"medium";a:5:{s:4:"file";s:78:"Automated_MRI_Quality_Assessment_CDW_Artefact_Simulation_FLAIR-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:8528;}s:5:"large";a:5:{s:4:"file";s:79:"Automated_MRI_Quality_Assessment_CDW_Artefact_Simulation_FLAIR-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:40686;}s:9:"thumbnail";a:5:{s:4:"file";s:78:"Automated_MRI_Quality_Assessment_CDW_Artefact_Simulation_FLAIR-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:5029;}}s:8:"filesize";i:568033;}'),
(2365, 2003, '_wp_attached_file', '2023/12/institut_cerveau_quad_uk-scaled.jpg'),
(2366, 2003, '_wp_attachment_metadata', 'a:7:{s:5:"width";i:2560;s:6:"height";i:792;s:4:"file";s:43:"2023/12/institut_cerveau_quad_uk-scaled.jpg";s:8:"filesize";i:205017;s:5:"sizes";a:6:{s:6:"medium";a:5:{s:4:"file";s:35:"institut_cerveau_quad_uk-300x93.jpg";s:5:"width";i:300;s:6:"height";i:93;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:36773;}s:5:"large";a:5:{s:4:"file";s:37:"institut_cerveau_quad_uk-1024x317.jpg";s:5:"width";i:1024;s:6:"height";i:317;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:88848;}s:9:"thumbnail";a:5:{s:4:"file";s:36:"institut_cerveau_quad_uk-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:31468;}s:12:"medium_large";a:5:{s:4:"file";s:36:"institut_cerveau_quad_uk-768x238.jpg";s:5:"width";i:768;s:6:"height";i:238;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:70536;}s:9:"1536x1536";a:5:{s:4:"file";s:37:"institut_cerveau_quad_uk-1536x475.jpg";s:5:"width";i:1536;s:6:"height";i:475;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:125403;}s:9:"2048x2048";a:5:{s:4:"file";s:37:"institut_cerveau_quad_uk-2048x633.jpg";s:5:"width";i:2048;s:6:"height";i:633;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:164651;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}s:14:"original_image";s:28:"institut_cerveau_quad_uk.jpg";}'),
(2367, 2004, '_wp_attached_file', '2023/12/inr_logo_rouge.jpg'),
(2368, 2004, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1289;s:6:"height";i:453;s:4:"file";s:26:"2023/12/inr_logo_rouge.jpg";s:8:"filesize";i:122489;s:5:"sizes";a:4:{s:6:"medium";a:5:{s:4:"file";s:26:"inr_logo_rouge-300x105.jpg";s:5:"width";i:300;s:6:"height";i:105;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:30358;}s:5:"large";a:5:{s:4:"file";s:27:"inr_logo_rouge-1024x360.jpg";s:5:"width";i:1024;s:6:"height";i:360;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:54103;}s:9:"thumbnail";a:5:{s:4:"file";s:26:"inr_logo_rouge-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:28024;}s:12:"medium_large";a:5:{s:4:"file";s:26:"inr_logo_rouge-768x270.jpg";s:5:"width";i:768;s:6:"height";i:270;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:45235;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2369, 2005, '_wp_attached_file', '2023/12/LOGO_CNRS_BLEU.png'),
(2370, 2005, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:875;s:6:"height";i:863;s:4:"file";s:26:"2023/12/LOGO_CNRS_BLEU.png";s:8:"filesize";i:19008;s:5:"sizes";a:3:{s:6:"medium";a:5:{s:4:"file";s:26:"LOGO_CNRS_BLEU-300x296.png";s:5:"width";i:300;s:6:"height";i:296;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:21484;}s:9:"thumbnail";a:5:{s:4:"file";s:26:"LOGO_CNRS_BLEU-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:10352;}s:12:"medium_large";a:5:{s:4:"file";s:26:"LOGO_CNRS_BLEU-768x757.png";s:5:"width";i:768;s:6:"height";i:757;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:56912;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2371, 2006, '_wp_attached_file', '2023/12/Logo_AP-Hopitaux_de_Paris.gif'),
(2372, 2006, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1288;s:6:"height";i:268;s:4:"file";s:37:"2023/12/Logo_AP-Hopitaux_de_Paris.gif";s:8:"filesize";i:9814;s:5:"sizes";a:4:{s:6:"medium";a:5:{s:4:"file";s:36:"Logo_AP-Hopitaux_de_Paris-300x62.gif";s:5:"width";i:300;s:6:"height";i:62;s:9:"mime-type";s:9:"image/gif";s:8:"filesize";i:1588;}s:5:"large";a:5:{s:4:"file";s:38:"Logo_AP-Hopitaux_de_Paris-1024x213.gif";s:5:"width";i:1024;s:6:"height";i:213;s:9:"mime-type";s:9:"image/gif";s:8:"filesize";i:7824;}s:9:"thumbnail";a:5:{s:4:"file";s:37:"Logo_AP-Hopitaux_de_Paris-150x150.gif";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/gif";s:8:"filesize";i:853;}s:12:"medium_large";a:5:{s:4:"file";s:37:"Logo_AP-Hopitaux_de_Paris-768x160.gif";s:5:"width";i:768;s:6:"height";i:160;s:9:"mime-type";s:9:"image/gif";s:8:"filesize";i:5515;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2375, 2013, '_wp_attached_file', '2018/11/camillaMannino.jpg'),
(2376, 2013, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:533;s:6:"height";i:712;s:4:"file";s:26:"2018/11/camillaMannino.jpg";s:8:"filesize";i:83443;s:5:"sizes";a:2:{s:6:"medium";a:5:{s:4:"file";s:26:"camillaMannino-225x300.jpg";s:5:"width";i:225;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:14250;}s:9:"thumbnail";a:5:{s:4:"file";s:26:"camillaMannino-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:5644;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2377, 2014, '_wp_attached_file', '2018/11/FedericaCacciamani.jpg'),
(2378, 2014, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1920;s:6:"height";i:1280;s:4:"file";s:30:"2018/11/FedericaCacciamani.jpg";s:8:"filesize";i:158603;s:5:"sizes";a:5:{s:6:"medium";a:5:{s:4:"file";s:30:"FedericaCacciamani-300x200.jpg";s:5:"width";i:300;s:6:"height";i:200;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:5627;}s:5:"large";a:5:{s:4:"file";s:31:"FedericaCacciamani-1024x683.jpg";s:5:"width";i:1024;s:6:"height";i:683;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:34416;}s:9:"thumbnail";a:5:{s:4:"file";s:30:"FedericaCacciamani-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:3339;}s:12:"medium_large";a:5:{s:4:"file";s:30:"FedericaCacciamani-768x512.jpg";s:5:"width";i:768;s:6:"height";i:512;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:21809;}s:9:"1536x1536";a:5:{s:4:"file";s:32:"FedericaCacciamani-1536x1024.jpg";s:5:"width";i:1536;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:65423;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2379, 2015, '_wp_attached_file', '2018/11/photos_Thibault_de_Varax.jpeg'),
(2380, 2015, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:1920;s:6:"height";i:1929;s:4:"file";s:37:"2018/11/photos_Thibault_de_Varax.jpeg";s:8:"filesize";i:405061;s:5:"sizes";a:5:{s:6:"medium";a:5:{s:4:"file";s:37:"photos_Thibault_de_Varax-300x300.jpeg";s:5:"width";i:300;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:9477;}s:5:"large";a:5:{s:4:"file";s:39:"photos_Thibault_de_Varax-1019x1024.jpeg";s:5:"width";i:1019;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:77964;}s:9:"thumbnail";a:5:{s:4:"file";s:37:"photos_Thibault_de_Varax-150x150.jpeg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:3110;}s:12:"medium_large";a:5:{s:4:"file";s:37:"photos_Thibault_de_Varax-768x772.jpeg";s:5:"width";i:768;s:6:"height";i:772;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:48381;}s:9:"1536x1536";a:5:{s:4:"file";s:39:"photos_Thibault_de_Varax-1529x1536.jpeg";s:5:"width";i:1529;s:6:"height";i:1536;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:159342;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2381, 2017, '_wp_attached_file', '2024/04/PhD_EDS-v1.pdf'),
(2382, 2017, '_wp_attachment_metadata', 'a:2:{s:5:"sizes";a:4:{s:4:"full";a:5:{s:4:"file";s:18:"PhD_EDS-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:65350;}s:6:"medium";a:5:{s:4:"file";s:26:"PhD_EDS-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:7909;}s:5:"large";a:5:{s:4:"file";s:27:"PhD_EDS-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:36581;}s:9:"thumbnail";a:5:{s:4:"file";s:26:"PhD_EDS-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:4896;}}s:8:"filesize";i:545600;}'),
(2384, 2022, '_wp_attached_file', '2024/05/InsermSeul_Rvb__noir.png'),
(2385, 2022, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:929;s:6:"height";i:316;s:4:"file";s:32:"2024/05/InsermSeul_Rvb__noir.png";s:8:"filesize";i:8672;s:5:"sizes";a:3:{s:6:"medium";a:5:{s:4:"file";s:32:"InsermSeul_Rvb__noir-300x102.png";s:5:"width";i:300;s:6:"height";i:102;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:5055;}s:9:"thumbnail";a:5:{s:4:"file";s:32:"InsermSeul_Rvb__noir-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:2976;}s:12:"medium_large";a:5:{s:4:"file";s:32:"InsermSeul_Rvb__noir-768x261.png";s:5:"width";i:768;s:6:"height";i:261;s:9:"mime-type";s:9:"image/png";s:8:"filesize";i:13813;}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'),
(2389, 2025, '_wp_attached_file', '2024/06/these-benchmarking-validation.pdf'),
(2390, 2025, '_wp_attachment_metadata', 'a:2:{s:5:"sizes";a:4:{s:4:"full";a:5:{s:4:"file";s:37:"these-benchmarking-validation-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:69892;}s:6:"medium";a:5:{s:4:"file";s:45:"these-benchmarking-validation-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:7716;}s:5:"large";a:5:{s:4:"file";s:46:"these-benchmarking-validation-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:36946;}s:9:"thumbnail";a:5:{s:4:"file";s:45:"these-benchmarking-validation-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";s:8:"filesize";i:4644;}}s:8:"filesize";i:324107;}'),
(2391, 2025, '_edit_lock', '1719573056:1');
# End of data contents of table `wp_aramis_postmeta`
# Table: `wp_aramis_posts`
# Delete any existing table `wp_aramis_posts`
DROP TABLE IF EXISTS `wp_aramis_posts`;
# Table structure of table `wp_aramis_posts`
CREATE TABLE `wp_aramis_posts` (
`ID` bigint(20) unsigned NOT NULL AUTO_INCREMENT,
`post_author` bigint(20) unsigned NOT NULL DEFAULT '0',
`post_date` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',
`post_date_gmt` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',
`post_content` longtext NOT NULL,
`post_title` text NOT NULL,
`post_excerpt` text NOT NULL,
`post_status` varchar(20) NOT NULL DEFAULT 'publish',
`comment_status` varchar(20) NOT NULL DEFAULT 'open',
`ping_status` varchar(20) NOT NULL DEFAULT 'open',
`post_password` varchar(255) NOT NULL DEFAULT '',
`post_name` varchar(200) NOT NULL DEFAULT '',
`to_ping` text NOT NULL,
`pinged` text NOT NULL,
`post_modified` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',
`post_modified_gmt` datetime NOT NULL DEFAULT '0000-00-00 00:00:00',
`post_content_filtered` longtext NOT NULL,
`post_parent` bigint(20) unsigned NOT NULL DEFAULT '0',
`guid` varchar(255) NOT NULL DEFAULT '',
`menu_order` int(11) NOT NULL DEFAULT '0',
`post_type` varchar(20) NOT NULL DEFAULT 'post',
`post_mime_type` varchar(100) NOT NULL DEFAULT '',
`comment_count` bigint(20) NOT NULL DEFAULT '0',
PRIMARY KEY (`ID`),
KEY `type_status_date` (`post_type`,`post_status`,`post_date`,`ID`),
KEY `post_parent` (`post_parent`),
KEY `post_author` (`post_author`),
KEY `post_name` (`post_name`(191))
) ENGINE=MyISAM AUTO_INCREMENT=2030 DEFAULT CHARSET=utf8 ;
# Data contents of table `wp_aramis_posts`
# Approximate rows expected in table: 1445
INSERT INTO `wp_aramis_posts` VALUES (4, 1, '2014-02-06 09:05:16', '2014-02-06 09:05:16', '
Faculty
[tmm name="faculty"]
Post-docs
[tmm name="post-docs"]
PhD Students
[tmm name="phd-students"]
Engineers
[tmm name="engineers"]
Associate fellows
[tmm name="collaborators"]
Administrative staff
[tmm name="assistant"]
Former Members
Left in 2024:
Ghislain Vaillant - Former Research engineer
Left in 2023:
Benoit Sauty - Former PhD student. Now research engineer at Owkin
Charley Presigny - Former PhD student. Now Postdoctoral fellow at University of Padova
Vito Dichio - Former PhD student. Now Postdoctoral fellow at ENS
Juliana Gonzalez - Former PhD student & Postdoctoral fellow
Remy Ben Messaoud - Former PhD student
Sophie Skriabine - Former PhD student
Lydia Chougar - Former PhD student
Anuradha Kar - Former Postdoctoral fellow
Janan Arslan - Former Postdoctoral fellow. Now deep learning engineer at Paris Brain Institute
Nadine Hamieh - Former Postdoctoral fellow
Rosana El Juridi - Former Postdoctoral fellow
Mauricio Diaz Melo - Former Research engineer
Etienne Maheux - Former Research engineer
Charlotte Dubec - Former Research engineer
Left in 2022:
Thibault Rolland - Former engineer
Clément Mantoux - Former PhD student
Virgilio Kmetzsch - Former PhD student. Now research engineer at Owkin
Johann Faouzi - Former PhD student & Postdoctoral fellow. Now lecturer at ENSAI Rennes
Dario Saracino - Former PhD student
Simona Bottani - Former PhD student. Now Postdoctoral fellow at Helmholtz Zentrum, Munich, Germany
Omar El Rifai - Former Research engineer
Left in 2021:
Elina Thibeau-Sutre - Former PhD student. Now Postdoctoral fellow at Twente University, Enschede, Netherland
Federica Cacciamani - Former PhD student
Arnaud Valladier - Former engineer
Paul Vernhet - Former PhD student
Tiziana Cattai - Former PhD student
Raphaël Couronné - Former PhD student
Stéphane Epelbaum - Former Faculty
Left in 2020:
Alexandre Bône - Former PhD student. Now Augmented Intelligence Specialist at Guerbet
Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
Arnaud Marcoux - Former engineer
Adam Wild - Former Engineer
Vincent Henry - Former Postdoctoral fellow
Emmannuel Mauduit - LinkedIn - Former coordinator the ICM center of Neuroinformatics
Thomas Lartigue - Former PhD student
Giulia Bassignana - Former PhD student
Alexandre Routier - Former engineer
Wen Wei - Former PhD student
Left in 2019 :
Maxime Louis - Former PhD Student, now at Pixyl
Pascal Lu - Former PhD student
Benoit Martin - Former engineer. Now PhD student
Alexis Guyot - Former Postdoctoral fellow
Junhao Wen - Former PhD student
Fanny Grosselin - Former PhD student
Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F,
Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. Paper in PDF
Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publication overview', '', 'publish', 'open', 'closed', '', 'publication-overview', '', '', '2021-01-08 14:29:58', '2021-01-08 13:29:58', '', 0, 'https://www.aramislab.fr/?page_id=26', 4, 'page', '',
(29, 1, '2014-02-06 11:00:26', '2014-02-06 10:00:26', '', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-06 11:00:26', '2014-02-06 10:00:26', '', 26, 'https://www.aramislab.fr/?p=29', 0, 'revision', '',
(30, 1, '2014-02-06 11:00:36', '2014-02-06 10:00:36', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, De Vico Fallani F, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'open', 'open', '', '26-autosave-v1', '', '', '2016-04-11 21:18:19', '2016-04-11 20:18:19', '', 26, 'https://www.aramislab.fr/?p=98', 0, 'revision', '',
(99, 1, '2014-03-03 12:16:10', '2014-03-03 11:16:10', '
[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 13:56:17', '2014-03-04 12:56:17', '', 26, 'https://www.aramislab.fr/?p=239', 0, 'revision', '',
(241, 1, '2016-02-01 10:38:22', '2016-02-01 09:38:22', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Modeling brain structure from anatomical and diffusion MRI
HERE TEXT
[su_lightbox type="image" src="https://www.aramislab.fr/wp-content/uploads/2014/03/Immagine_1_piccola.png"][su_button] Click Here to Watch the Video [/su_button][/su_lightbox]
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-05 21:16:04', '2014-03-05 20:16:04', '', 30, 'https://www.aramislab.fr/?p=331', 0, 'revision', '',
(332, 1, '2014-03-05 22:58:40', '2014-03-05 21:58:40', 'If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have open positions other than those advertised below.
', 'Research topics', '', 'inherit', 'closed', 'open', '', '22-revision-v1', '', '', '2014-03-06 09:41:06', '2014-03-06 08:41:06', '', 22, 'https://www.aramislab.fr/?p=342', 0, 'revision', '',
(343, 1, '2014-03-06 09:41:49', '2014-03-06 08:41:49', 'If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below.
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-06 09:41:49', '2014-03-06 08:41:49', '', 30, 'https://www.aramislab.fr/?p=343', 0, 'revision', '',
(344, 1, '2014-03-06 09:42:37', '2014-03-06 08:42:37', 'If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below.
IM2A / ICM Bruno Dubois\'s team (Bruno Dubois, Harald Hampel)
ICM Alexis Brice\'s team (Alexis Brice, Isabelle Le Ber, Christel Depienne)
', 'Research topics', '', 'inherit', 'closed', 'open', '', '22-revision-v1', '', '', '2014-03-07 14:48:00', '2014-03-07 13:48:00', '', 22, 'https://www.aramislab.fr/?p=368', 0, 'revision', '',
(369, 1, '2014-03-07 14:54:33', '2014-03-07 13:54:33', 'If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below.
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-07 14:54:33', '2014-03-07 13:54:33', '', 30, 'https://www.aramislab.fr/?p=369', 0, 'revision', '',
(370, 1, '2014-03-07 14:54:48', '2014-03-07 13:54:48', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below.
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-07 14:54:48', '2014-03-07 13:54:48', '', 30, 'https://www.aramislab.fr/?p=370', 0, 'revision', '',
(371, 1, '2014-03-07 21:07:31', '2014-03-07 20:07:31', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry.
Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O. The ADNI, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):682-96
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 2011 Jun;30(6):1214-27. Epub 2011 Jan 28.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; Alzheimer\'s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. 2009b. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46(3):749-61.
Chupin M, Gerardin E, Cuingnet R, Boutet C, Lemieux L, Lehericy S, Benali H, Garnero L, Colliot O. 2009a. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6):579-87.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 2012;7(11):e48953.
Publications from HAL
Here is a link to our publications on HAL.
[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Englais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL]', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-09 17:58:47', '2014-03-09 16:58:47', '', 26, 'https://www.aramislab.fr/?p=380', 0, 'revision', '',
(381, 1, '2014-03-09 18:20:45', '2014-03-09 17:20:45', 'Our team design automatic methods to segment anatomical structures from MRI data. Such approaches allow extrac', 'Segmentation of anatomical structures', '', 'inherit', 'closed', 'open', '', '305-autosave-v1', '', '', '2014-03-09 18:20:45', '2014-03-09 17:20:45', '', 305, 'https://www.aramislab.fr/?p=381', 0, 'revision', '',
(382, 1, '2014-03-09 18:21:35', '2014-03-09 17:21:35', 'Our team design automatic methods to segment anatomical structures from MRI data.', 'Segmentation of anatomical structures', '', 'inherit', 'closed', 'open', '', '305-revision-v1', '', '', '2014-03-09 18:21:35', '2014-03-09 17:21:35', '', 305, 'https://www.aramislab.fr/?p=382', 0, 'revision', '',
(383, 1, '2014-03-09 18:25:26', '2014-03-09 17:25:26', 'Our team develops a framework for the statistical analysis of anatomical shapes based on diffeomorphic deformations. The framework allows estimating a template which is representative of the population', 'Statistical models of anatomical shapes', '', 'inherit', 'closed', 'open', '', '352-autosave-v1', '', '', '2014-03-09 18:25:26', '2014-03-09 17:25:26', '', 352, 'https://www.aramislab.fr/?p=383', 0, 'revision', '',
(384, 1, '2014-03-09 18:25:58', '2014-03-09 17:25:58', 'Our team develops a framework for the statistical analysis of anatomical shapes based on diffeomorphic deformations. The framework allows estimating a template which is representative of the population and analyzing the variability of the population.', 'Statistical models of anatomical shapes', '', 'inherit', 'closed', 'open', '', '352-revision-v1', '', '', '2014-03-09 18:25:58', '2014-03-09 17:25:58', '', 352, 'https://www.aramislab.fr/?p=384', 0, 'revision', '',
(385, 1, '2014-03-09 18:31:14', '2014-03-09 17:31:14', '', 'Modeling brain networks', '', 'inherit', 'closed', 'open', '', '308-autosave-v1', '', '', '2014-03-09 18:31:14', '2014-03-09 17:31:14', '', 308, 'https://www.aramislab.fr/?p=385', 0, 'revision', '',
(386, 1, '2014-03-09 18:32:15', '2014-03-09 17:32:15', '', 'Modeling brain networks', '', 'inherit', 'closed', 'open', '', '308-revision-v1', '', '', '2014-03-09 18:32:15', '2014-03-09 17:32:15', '', 308, 'https://www.aramislab.fr/?p=386', 0, 'revision', '',
(387, 1, '2014-03-09 18:36:26', '2014-03-09 17:36:26', '
Context and general aim
Understanding brain function and its alterations requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets (e.g. Alzheimer’s disease neuroimaging initiative [ADNI], gene expression atlases from the Allen Institute...). In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (mainly neurodegenerative diseases, epilepsy and cerebrovascular disorders) in close collaboration with neuroradiologists and neurologists, in order to: i) provide insight into their physiopathology; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Understanding brain function and its alterations requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets (e.g. Alzheimer’s disease neuroimaging initiative [ADNI], gene expression atlases from the Allen Institute...). In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (mainly neurodegenerative diseases, epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Understanding brain function and its alterations requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets (e.g. Alzheimer’s disease neuroimaging initiative [ADNI], gene expression atlases from the Allen Institute...). In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (mainly neurodegenerative diseases, epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies at the best level. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in AD, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects at the earliest stage of the disease).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control, development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Dedicated engineers have been recruited to further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (mainly neurodegenerative diseases, epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Functional imaging techniques (EEG, MEG and fMRI) allow to characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in AD, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects at the earliest stage of the disease).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Dedicated engineers have been recruited to further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (such as Alzheimer\'s disease, , epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRIModeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (such as Alzheimer\'s disease, , epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRIModeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies (such as Alzheimer\'s disease, , epilepsy and cerebrovascular disorders) in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRIModeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRIModeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals.
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining brain imaging, “omics”, electrophysiology, cognitive tests, clinical data, are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O. The ADNI, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):682-96
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 2011 Jun;30(6):1214-27. Epub 2011 Jan 28.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; Alzheimer\'s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011
Publications from HAL
Here is a link to our publications on HAL.
[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Englais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL]', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-09 19:55:02', '2014-03-09 18:55:02', '', 26, 'https://www.aramislab.fr/?p=400', 0, 'revision', '',
(403, 1, '2014-03-09 20:15:30', '2014-03-09 19:15:30', '
Context and general aim
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining brain imaging, “omics”, electrophysiology, cognitive tests, clinical data, are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
IM2A / ICM Bruno Dubois\'s team (Bruno Dubois, Harald Hampel, Stéphane Epelbaum)
ICM Alexis Brice\'s team (Alexis Brice, Isabelle Le Ber, Christel Depienne)
ICM Marie Vidailhet / Stéphane Lehéricy\'s team (Marie Vidailhet, Stéphane Lehéricy, Andreas Hartmann, Yulia Worbe)
ICM Richard Miles\'s team
ICM Stéphane Charpier\'s team
Department of Neuroradiology, Pitié-Salpêtrière Hospital
', 'Research topics', '', 'inherit', 'closed', 'open', '', '22-revision-v1', '', '', '2014-03-09 20:26:44', '2014-03-09 19:26:44', '', 22, 'https://www.aramislab.fr/?p=405', 0, 'revision', '',
(406, 1, '2014-03-09 20:30:06', '2014-03-09 19:30:06', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-09 20:30:06', '2014-03-09 19:30:06', '', 30, 'https://www.aramislab.fr/?p=406', 0, 'revision', '',
(407, 1, '2014-03-09 20:31:40', '2014-03-09 19:31:40', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-09 20:31:40', '2014-03-09 19:31:40', '', 30, 'https://www.aramislab.fr/?p=407', 0, 'revision', '',
(408, 1, '2014-03-09 20:32:35', '2014-03-09 19:32:35', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-09 20:32:35', '2014-03-09 19:32:35', '', 30, 'https://www.aramislab.fr/?p=408', 0, 'revision', '',
(410, 1, '2014-03-10 10:08:41', '2014-03-10 09:08:41', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-10 10:08:41', '2014-03-10 09:08:41', '', 30, 'https://www.aramislab.fr/?p=410', 0, 'revision', '',
(411, 1, '2014-03-10 10:09:19', '2014-03-10 09:09:19', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software. We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Medical collaborations
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O. The ADNI, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):682-96
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 2011 Jun;30(6):1214-27. Epub 2011 Jan 28.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; Alzheimer\'s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011
Publications from HAL
Here is a link to our publications on HAL.
[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Englais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-10 11:31:35', '2014-03-10 10:31:35', '', 26, 'https://www.aramislab.fr/?p=423', 0, 'revision', '',
(425, 1, '2014-03-10 14:37:59', '2014-03-10 13:37:59', '
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
External collaborations
Methodological collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Medical collaborations
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
Local collaborations
Methodological collaborations
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O. The ADNI, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):682-96
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 2011 Jun;30(6):1214-27. Epub 2011 Jan 28.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; Alzheimer\'s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011
Publications from HAL
Here is a link to our publications on HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 09:11:07', '2014-03-11 08:11:07', '', 26, 'https://www.aramislab.fr/?p=431', 0, 'revision', '',
(435, 1, '2014-03-11 15:47:25', '2014-03-11 14:47:25', '
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Collaborations
ENS de Cachan (Alain Trouvé)
Université Paris-Descartes (Joan Glaunès)
Center for Applied Medical Research, Pampelune, Spain (M. Valencia)
Departement of Physics. Queen Mary University of London, UK (V. Latora)
INRIA Asclepios (Nicholas Ayache)
CEA Neurospin (Jean-François Mangin, Cyril Poupon, Vincent Frouin, Lucie Hertz-Pannier, Jean-Baptiste Poline)
Center for Magnetic Resonance Research, University of Minnesota, USA (Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil)
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA (Guido Gerig, Sarang Joshi, Marcel Prastawa)
INRIA Visages (Christian Barillot)
Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
Institut Pasteur, Paris (Roberto Toro)
Department of Ecology and Evolution, ENS Ulm, Paris (B. Cazelles)
Anatomical Neuropharmacology Unit, University of Oxford, UK (J. Mena-Segovia)
Massachusetts General Hospital, Harvard University, USA (R. Gupta)
Department of Neurology, Lariboisière Hospital, Paris (Hugues Chabriat)
Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
Kremlin-Bicêtre Hospital, Paris (Emmanuelle Corruble, Florence Gressier, Romain Colle)
Bordeaux University Hospital (Carole Dufouil)
Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
Lille University Hospital (Christine Delmaire)
CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O. The ADNI, Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3):682-96
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 2011 Jun;30(6):1214-27. Epub 2011 Jan 28.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O; Alzheimer\'s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 17:48:06', '2014-03-11 16:48:06', '', 26, 'https://www.aramislab.fr/?p=448', 0, 'revision', '',
(449, 1, '2014-03-11 17:49:51', '2014-03-11 16:49:51', '
Context and general aim
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited.
TO BE COMPLETED
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:39:38', '2014-03-11 20:39:38', '', 26, 'https://www.aramislab.fr/?p=460', 0, 'revision', '',
(462, 1, '2014-03-11 21:52:56', '2014-03-11 20:52:56', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:52:56', '2014-03-11 20:52:56', '', 26, 'https://www.aramislab.fr/?p=462', 0, 'revision', '',
(463, 1, '2014-03-11 21:53:18', '2014-03-11 20:53:18', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:53:18', '2014-03-11 20:53:18', '', 26, 'https://www.aramislab.fr/?p=463', 0, 'revision', '',
(464, 1, '2014-03-11 21:53:43', '2014-03-11 20:53:43', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:53:43', '2014-03-11 20:53:43', '', 26, 'https://www.aramislab.fr/?p=464', 0, 'revision', '',
(465, 1, '2014-03-11 21:55:09', '2014-03-11 20:55:09', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:55:09', '2014-03-11 20:55:09', '', 26, 'https://www.aramislab.fr/?p=465', 0, 'revision', '',
(466, 1, '2014-03-11 21:55:44', '2014-03-11 20:55:44', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:55:44', '2014-03-11 20:55:44', '', 26, 'https://www.aramislab.fr/?p=466', 0, 'revision', '',
(467, 1, '2014-03-11 21:58:17', '2014-03-11 20:58:17', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:58:17', '2014-03-11 20:58:17', '', 26, 'https://www.aramislab.fr/?p=467', 0, 'revision', '',
(468, 1, '2014-03-11 21:59:46', '2014-03-11 20:59:46', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 21:59:46', '2014-03-11 20:59:46', '', 26, 'https://www.aramislab.fr/?p=468', 0, 'revision', '',
(469, 1, '2014-03-11 22:00:47', '2014-03-11 21:00:47', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:00:47', '2014-03-11 21:00:47', '', 26, 'https://www.aramislab.fr/?p=469', 0, 'revision', '',
(470, 1, '2014-03-11 22:01:03', '2014-03-11 21:01:03', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:01:03', '2014-03-11 21:01:03', '', 26, 'https://www.aramislab.fr/?p=470', 0, 'revision', '',
(471, 1, '2014-03-11 22:01:33', '2014-03-11 21:01:33', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:01:33', '2014-03-11 21:01:33', '', 26, 'https://www.aramislab.fr/?p=471', 0, 'revision', '',
(472, 1, '2014-03-11 22:01:51', '2014-03-11 21:01:51', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:01:51', '2014-03-11 21:01:51', '', 26, 'https://www.aramislab.fr/?p=472', 0, 'revision', '',
(473, 1, '2014-03-11 22:02:17', '2014-03-11 21:02:17', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:02:17', '2014-03-11 21:02:17', '', 26, 'https://www.aramislab.fr/?p=473', 0, 'revision', '',
(474, 1, '2014-03-11 22:06:35', '2014-03-11 21:06:35', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:06:35', '2014-03-11 21:06:35', '', 26, 'https://www.aramislab.fr/?p=474', 0, 'revision', '',
(475, 1, '2014-03-11 22:07:06', '2014-03-11 21:07:06', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:07:06', '2014-03-11 21:07:06', '', 26, 'https://www.aramislab.fr/?p=475', 0, 'revision', '',
(476, 1, '2014-03-11 22:33:21', '2014-03-11 21:33:21', '
Most representative publications
HERE : PUT LIST OF MOST REPRESENTATIVE PUBLICATIONS.
USE FOLLOWING FORMAT
Olivier
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
C.1. Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 2012 Dec;117(6):1300-10.
C.1. Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes.2011 Nov;97(1-2):170-82
C.1. Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74: 1946-53 (2010).
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-11 22:33:21', '2014-03-11 21:33:21', '', 26, 'https://www.aramislab.fr/?p=476', 0, 'revision', '',
(478, 1, '2014-03-12 15:45:03', '2014-03-12 14:45:03', '
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
C.1. Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 2012 Dec;117(6):1300-10.
C.1. Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes.2011 Nov;97(1-2):170-82
C.1. Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74: 1946-53 (2010).
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 15:46:50', '2014-03-12 14:46:50', '', 26, 'https://www.aramislab.fr/?p=479', 0, 'revision', '',
(480, 1, '2014-03-12 15:50:20', '2014-03-12 14:50:20', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
C.1. Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes.2011 Nov;97(1-2):170-82
C.1. Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74: 1946-53 (2010).
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 15:50:20', '2014-03-12 14:50:20', '', 26, 'https://www.aramislab.fr/?p=480', 0, 'revision', '',
(481, 1, '2014-03-12 15:52:48', '2014-03-12 14:52:48', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 15:52:48', '2014-03-12 14:52:48', '', 26, 'https://www.aramislab.fr/?p=481', 0, 'revision', '',
(482, 1, '2014-03-12 15:54:25', '2014-03-12 14:54:25', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
S. Durrleman, X. Pennec, A. Trouvé, J. Braga, G. Gerig, N. Ayache, Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision, 103(1):22-59, 2013. Paper in PDF
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 15:54:25', '2014-03-12 14:54:25', '', 26, 'https://www.aramislab.fr/?p=482', 0, 'revision', '',
(483, 1, '2014-03-12 15:56:41', '2014-03-12 14:56:41', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
S. Durrleman, X. Pennec, A. Trouvé, J. Braga, G. Gerig, N. Ayache, Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision, 103(1):22-59, 2013. Paper in PDF
S. Durrleman, X. Pennec, A. Trouvé, P. Thompson, N. Ayache, Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis, 12(5):626-637, 2008. Paper in PDF
S. Durrleman, P. Fillard, X. Pennec, A. Trouvé, N. Ayache, Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 15:56:41', '2014-03-12 14:56:41', '', 26, 'https://www.aramislab.fr/?p=483', 0, 'revision', '',
(484, 1, '2014-03-12 16:01:11', '2014-03-12 15:01:11', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision, 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis, 12(5):626-637, 2008. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett, 110: 174102, 2013.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett, 104:118701, 2010.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:01:11', '2014-03-12 15:01:11', '', 26, 'https://www.aramislab.fr/?p=484', 0, 'revision', '', 0);
INSERT INTO `wp_aramis_posts` VALUES (485, 1, '2014-03-12 16:02:03', '2014-03-12 15:02:03', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision, 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis, 12(5):626-637, 2008. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett, 110: 174102, 2013.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett, 104:118701, 2010.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:02:03', '2014-03-12 15:02:03', '', 26, 'https://www.aramislab.fr/?p=485', 0, 'revision', '',
(486, 1, '2014-03-12 16:02:19', '2014-03-12 15:02:19', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology 74:1946-53, 2010.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision, 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis, 12(5):626-637, 2008. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett, 110: 174102, 2013.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett, 104:118701, 2010.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:02:19', '2014-03-12 15:02:19', '', 26, 'https://www.aramislab.fr/?p=486', 0, 'revision', '',
(487, 1, '2014-03-12 16:03:31', '2014-03-12 15:03:31', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:03:31', '2014-03-12 15:03:31', '', 26, 'https://www.aramislab.fr/?p=487', 0, 'revision', '',
(488, 1, '2014-03-12 16:07:26', '2014-03-12 15:07:26', '
Most representative publications
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:07:26', '2014-03-12 15:07:26', '', 26, 'https://www.aramislab.fr/?p=488', 0, 'revision', '',
(489, 1, '2014-03-12 16:11:10', '2014-03-12 15:11:10', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:11:10', '2014-03-12 15:11:10', '', 26, 'https://www.aramislab.fr/?p=489', 0, 'revision', '',
(490, 1, '2014-03-12 16:11:22', '2014-03-12 15:11:22', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 16:11:22', '2014-03-12 15:11:22', '', 26, 'https://www.aramislab.fr/?p=490', 0, 'revision', '',
(491, 1, '2014-03-12 16:15:11', '2014-03-12 15:15:11', '
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46(3):749-61, 2009.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 17:39:41', '2014-03-12 16:39:41', '', 26, 'https://www.aramislab.fr/?p=495', 0, 'revision', '',
(496, 1, '2014-03-12 17:40:46', '2014-03-12 16:40:46', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 17:40:46', '2014-03-12 16:40:46', '', 26, 'https://www.aramislab.fr/?p=496', 0, 'revision', '',
(497, 1, '2014-03-12 17:41:56', '2014-03-12 16:41:56', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012.
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 17:41:56', '2014-03-12 16:41:56', '', 26, 'https://www.aramislab.fr/?p=497', 0, 'revision', '',
(498, 1, '2014-03-12 17:42:42', '2014-03-12 16:42:42', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009.
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012.
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 17:42:42', '2014-03-12 16:42:42', '', 26, 'https://www.aramislab.fr/?p=498', 0, 'revision', '',
(499, 1, '2014-03-12 17:44:28', '2014-03-12 16:44:28', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-12 17:44:28', '2014-03-12 16:44:28', '', 26, 'https://www.aramislab.fr/?p=499', 0, 'revision', '',
(509, 1, '2014-03-13 10:11:34', '2014-03-13 09:11:34', 'Next seminar : for a seminar on March 27st, 2014.
Speaker : François Rousseau (CNRS/ Strasbourg University)
Seminar', 'Seminar - François Rousseau (CNRS/Strasbourg University) - March 27th 2014', '', 'inherit', 'closed', 'open', '', '446-autosave-v1', '', '', '2014-03-13 10:11:34', '2014-03-13 09:11:34', '', 446, 'https://www.aramislab.fr/?p=509', 0, 'revision', '',
(510, 1, '2014-03-13 10:12:03', '2014-03-13 09:12:03', 'Next seminar : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: TBA', 'Seminar - François Rousseau (CNRS/Strasbourg University) - March 27th 2014', '', 'inherit', 'closed', 'open', '', '446-revision-v1', '', '', '2014-03-13 10:12:03', '2014-03-13 09:12:03', '', 446, 'https://www.aramislab.fr/?p=510', 0, 'revision', '',
(511, 1, '2014-03-13 10:12:55', '2014-03-13 09:12:55', 'Next seminar : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: TBA', 'Seminar - François Rousseau (CNRS/Strasbourg University) - March 27th 2014', '', 'inherit', 'closed', 'open', '', '446-revision-v1', '', '', '2014-03-13 10:12:55', '2014-03-13 09:12:55', '', 446, 'https://www.aramislab.fr/?p=511', 0, 'revision', '',
(512, 1, '2014-03-13 10:33:25', '2014-03-13 09:33:25', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications since 2013
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-13 10:33:25', '2014-03-13 09:33:25', '', 26, 'https://www.aramislab.fr/?p=512', 0, 'revision', '',
(513, 1, '2014-03-13 10:34:33', '2014-03-13 09:34:33', '
Most representative publications
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'open', '', '26-revision-v1', '', '', '2014-03-13 10:34:33', '2014-03-13 09:34:33', '', 26, 'https://www.aramislab.fr/?p=513', 0, 'revision', '',
(514, 1, '2014-03-13 10:45:54', '2014-03-13 09:45:54', '
Context and general aim
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, ARAMIS is in charge of harmonization of MRI acquisitions, image quality control and development of image analysis methods and software (segmentation, morphometry, machine learning). We have designed and implemented harmonized MRI protocols for nearly 30 imaging centers in France. We implement procedures for controlling image quality across centers and develop the corresponding software. Engineers further develop our segmentation and morphometry software, to increase robustness and scalability.
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
INRIA official website features the research of the ARAMIS Lab on the occasion of the French "Brain week". Full article here (in French), and more pics there. Enjoy and Share!
Jan 8: Congratulations to Pietro Gori!
who successfully defended his PhD today about "Statistical models to learn the structural organisation of neural circuits from multimodal brain images, with application to Gilles de la Tourette syndrome".
Dec 21: Congratulations to Takoua Kaaouana!
who successfully defended her PhD today on the "detection and characterization of brain micro-bleeds with applications on mutli-centric clinical data".
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'publish', 'closed', 'open', '', 'news-2', '', '', '2016-03-15 11:27:32', '2016-03-15 10:27:32', '', 0, 'https://www.aramislab.fr/?page_id=521', 0, 'page', '',
(523, 1, '2014-03-13 14:29:05', '2014-03-13 13:29:05', '
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: To be announced', 'News', '', 'inherit', 'closed', 'open', '', '521-revision-v1', '', '', '2014-03-13 14:38:16', '2014-03-13 13:38:16', '', 521, 'https://www.aramislab.fr/?p=526', 0, 'revision', '',
(528, 1, '2014-03-13 15:45:42', '2014-03-13 14:45:42', '
Context and general aim
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of new tools for image analysis (segmentation, morphometry)
Studying brain structure and function requires the integration of multiple levels of organization, operating at different spatial and temporal scales. The integration of such a large variety of data is now possible thanks to the recent emergence of large-scale multimodal datasets. In this context, mathematical and computational approaches are becoming increasingly important because: i) they provide formalized, operational and flexible frameworks from integrating multiple processes and scales; ii) they allow automated processing and analysis of massive datasets. These approaches can then be used to find biomarkers of a disease, for genotype/phenotype correlations, or to characterize functional responses for instance.
The overall aim of our team is to design new computational and mathematical approaches for studying brain structure (based on anatomical and diffusion MRI) and functional connectivity (based on EEG, MEG and intracerebral recordings). The goal is to transform raw unstructured images and signals into formalized, operational models such as geometric models of brain structures, statistical population models, graph-theoretic models of brain connectivity...
These new approaches are applied to brain pathologies, in close collaboration with medical researchers and clinicians, in order to: i) better understand their pathophysiological mechanisms; ii) design new biomarkers for diagnosis, prognosis and tracking disease progression.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
', 'Team Members', '', 'inherit', 'closed', 'open', '', '4-revision-v1', '', '', '2014-03-23 18:45:40', '2014-03-23 17:45:40', '', 4, 'https://www.aramislab.fr/?p=558', 0, 'revision', '',
(559, 1, '2014-03-24 12:11:48', '2014-03-24 11:11:48', '', 'ARC neuroimagerie', '', 'inherit', 'closed', 'open', '', 'cdd_cati_7t_arc', '', '', '2014-03-24 12:11:48', '2014-03-24 11:11:48', '', 30, 'https://www.aramislab.fr/wp-content/uploads/2014/02/CDD_CATI_7T_ARC.pdf', 0, 'attachment', 'application/pdf',
(560, 1, '2014-03-24 12:12:17', '2014-03-24 11:12:17', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-24 12:12:17', '2014-03-24 11:12:17', '', 30, 'https://www.aramislab.fr/?p=560', 0, 'revision', '',
(561, 1, '2014-03-24 12:13:41', '2014-03-24 11:13:41', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-24 12:13:41', '2014-03-24 11:13:41', '', 30, 'https://www.aramislab.fr/?p=561', 0, 'revision', '',
(562, 1, '2014-03-24 12:14:42', '2014-03-24 11:14:42', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI', 'News', '', 'inherit', 'closed', 'open', '', '521-revision-v1', '', '', '2014-03-24 13:34:28', '2014-03-24 12:34:28', '', 521, 'https://www.aramislab.fr/?p=563', 0, 'revision', '',
(564, 1, '2014-03-24 13:42:55', '2014-03-24 12:42:55', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-24 13:42:55', '2014-03-24 12:42:55', '', 30, 'https://www.aramislab.fr/?p=564', 0, 'revision', '',
(565, 1, '2014-03-25 09:41:31', '2014-03-25 08:41:31', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 09:41:31', '2014-03-25 08:41:31', '', 30, 'https://www.aramislab.fr/?p=565', 0, 'revision', '',
(566, 1, '2014-03-25 09:41:45', '2014-03-25 08:41:45', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 09:41:45', '2014-03-25 08:41:45', '', 30, 'https://www.aramislab.fr/?p=566', 0, 'revision', '',
(567, 1, '2014-03-25 09:44:11', '2014-03-25 08:44:11', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 09:44:11', '2014-03-25 08:44:11', '', 30, 'https://www.aramislab.fr/?p=567', 0, 'revision', '',
(568, 1, '2014-03-25 09:44:52', '2014-03-25 08:44:52', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 09:44:52', '2014-03-25 08:44:52', '', 30, 'https://www.aramislab.fr/?p=568', 0, 'revision', '',
(570, 1, '2014-03-25 09:59:02', '2014-03-25 08:59:02', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 09:59:02', '2014-03-25 08:59:02', '', 30, 'https://www.aramislab.fr/?p=570', 0, 'revision', '',
(571, 1, '2014-03-25 10:00:41', '2014-03-25 09:00:41', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 10:00:41', '2014-03-25 09:00:41', '', 30, 'https://www.aramislab.fr/?p=571', 0, 'revision', '',
(578, 1, '2014-03-25 10:53:56', '2014-03-25 09:53:56', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'open', '', '30-revision-v1', '', '', '2014-03-25 10:53:56', '2014-03-25 09:53:56', '', 30, 'https://www.aramislab.fr/?p=578', 0, 'revision', '',
(579, 1, '2014-03-25 10:54:08', '2014-03-25 09:54:08', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2014-04-01 19:03:24', '2014-04-01 18:03:24', '', 4, 'https://www.aramislab.fr/?p=584', 0, 'revision', '',
(586, 1, '2014-04-10 11:41:00', '2014-04-10 10:41:00', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-04-10 11:41:00', '2014-04-10 10:41:00', '', 30, 'https://www.aramislab.fr/?p=586', 0, 'revision', '',
(587, 1, '2014-04-10 11:41:50', '2014-04-10 10:41:50', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-04-10 11:41:50', '2014-04-10 10:41:50', '', 30, 'https://www.aramislab.fr/?p=587', 0, 'revision', '',
(588, 1, '2014-04-10 11:43:02', '2014-04-10 10:43:02', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-04-10 11:43:02', '2014-04-10 10:43:02', '', 30, 'https://www.aramislab.fr/?p=588', 0, 'revision', '',
(590, 1, '2014-04-10 12:50:08', '2014-04-10 11:50:08', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-04-10 12:50:08', '2014-04-10 11:50:08', '', 30, 'https://www.aramislab.fr/?p=590', 0, 'revision', '',
(594, 1, '2014-04-25 08:30:51', '2014-04-25 07:30:51', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
INRIA official website features the research of the ARAMIS Lab on the occasion of the French "Brain week". Full article here (in French), and more pics there. Enjoy and Share!
Jan 8: Congratulations to Pietro Gori!
who successfully defended his PhD today about "Statistical models to learn the structural organisation of neural circuits from multimodal brain images, with application to Gilles de la Tourette syndrome".
Dec 21: Congratulations to Takoua Kaaouana!
who successfully defended her PhD today on the "detection and characterization of brain micro-bleeds with applications on mutli-centric clinical data".
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-autosave-v1', '', '', '2016-03-15 11:26:54', '2016-03-15 10:26:54', '', 521, 'https://www.aramislab.fr/?p=597', 0, 'revision', '',
(598, 3, '2014-06-24 19:31:03', '2014-06-24 18:31:03', '
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-06-24 19:31:03', '2014-06-24 18:31:03', '', 521, 'https://www.aramislab.fr/?p=598', 0, 'revision', '',
(606, 1, '2014-09-09 17:23:27', '2014-09-09 16:23:27', '
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2014-09-09 17:23:27', '2014-09-09 16:23:27', '', 4, 'https://www.aramislab.fr/?p=606', 0, 'revision', '',
(607, 1, '2014-09-09 17:30:43', '2014-09-09 16:30:43', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2014-09-10 08:53:20', '2014-09-10 07:53:20', '', 4, 'https://www.aramislab.fr/?p=612', 0, 'revision', '',
(616, 1, '2014-09-29 14:41:32', '2014-09-29 13:41:32', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-09-29 14:41:32', '2014-09-29 13:41:32', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(617, 1, '2014-09-29 14:42:24', '2014-09-29 13:42:24', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-09-29 14:42:24', '2014-09-29 13:42:24', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(618, 1, '2014-09-29 14:43:16', '2014-09-29 13:43:16', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-09-29 14:43:16', '2014-09-29 13:43:16', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(619, 1, '2014-09-29 17:17:24', '2014-09-29 16:17:24', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
A. Routier, N. Burgos, M. Díaz, M. Bacci, S. Bottani, O. El Rifai, S. Fontanella, P. Gori, J. Guillon, A. Guyot, R. Hassanaly, T. Jacquemont, P. Lu, A. Marcoux, T. Moreau, J. Samper-González, M. Teichmann. E. Thibeau-Sutre, G. Vaillant, J. Wen, A. Wild, M.-O. Habert, S. Durrleman, O. Colliot – Clinica: an open source software platform for reproducible clinical neuroscience studies, Frontiers in Neuroinformatics, 2021. Paper in PDF
Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Routier A, Marcoux A, Diaz Melo M, Samper-González J, Wild A, Guyot A, Wen J, Thibeau- Sutre E, Bottani S, Durrleman S, Burgos N, Colliot O: New Longitudinal and Deep Learning Pipelines in the Clinica Software Platform, OHBM, 2020. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data, Neuroimage, 183: 504–521, 2018. Paper in PDF
Marcoux A, Burgos N, Bertrand A, Teichmann M, Routier A, Wen J, Samper-González J, Bottani S, Durrleman S, Habert M-O, Colliot O: An Automated Pipeline for the Analysis of PET Data on the Cortical Surface, Frontiers in Neuroinformatics, 12, 2018. Paper in PDF
Leaspy is a software package for the statistical analysis of longitudinal data, particularly medical data that comes in a form of repeated observations of patients at different time-points. Considering these series of short-term data, the software aims at :
recombining them to reconstruct the long-term spatio-temporal trajectory of evolution
positioning each patient observations relatively to the group-average timeline, in term of both temporal differences (time shift and acceleration factor) and spatial differences (different sequences of events, spatial pattern of progression, ...)
quantifying impact of cofactors (gender, genetic mutation, environmental factors, ...) on the evolution of the signal
imputing missing values
predicting future observations
simulating virtual patients to un-bias the initial cohort or mimics its characteristics
References
I. Koval, A. Bone, M. Louis, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonniere & S. Durrleman, Intensive application for Alzheimer\'s Disease progression: AD Course Map charts Alzheimer\'s disease progression, Scientific Reports, 2021. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4], [5], [6]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
L
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-autosave-v1', '', '', '2015-10-09 09:03:43', '2015-10-09 08:03:43', '', 521, 'https://www.aramislab.fr/521-autosave-v1/', 0, 'revision', '', 0);
INSERT INTO `wp_aramis_posts` VALUES (638, 1, '2014-09-30 14:17:19', '2014-09-30 13:17:19', '
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-09-30 14:17:19', '2014-09-30 13:17:19', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(639, 1, '2014-09-30 14:17:34', '2014-09-30 13:17:34', '
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-09-30 14:17:34', '2014-09-30 13:17:34', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(640, 1, '2014-09-30 14:28:28', '2014-09-30 13:28:28', '
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Reference : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Graph analysis of functional brain networks: practical issues in translational neuroscience
Phil Trans R Soc B 2014 369: 20130521
Abstract :
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-09-30 14:28:28', '2014-09-30 13:28:28', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(641, 1, '2014-09-30 14:31:45', '2014-09-30 13:31:45', '
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link : Information, Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-09-30 14:31:45', '2014-09-30 13:31:45', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(642, 1, '2014-09-30 14:32:35', '2014-09-30 13:32:35', '
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link : Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2014-09-30 14:32:35', '2014-09-30 13:32:35', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(644, 1, '2014-10-08 10:47:38', '2014-10-08 09:47:38', '
Maxime Corduant - UPMC - maxime.corduant@ensiie.fr
Kevin Roussel - UPMC - kevin.roussel@upmc.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2014-11-04 10:40:02', '2014-11-04 09:40:02', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(650, 1, '2014-11-18 15:18:57', '2014-11-18 14:18:57', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:18:57', '2014-11-18 14:18:57', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(651, 1, '2014-11-18 15:24:54', '2014-11-18 14:24:54', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:24:54', '2014-11-18 14:24:54', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(652, 1, '2014-11-18 15:28:00', '2014-11-18 14:28:00', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:28:00', '2014-11-18 14:28:00', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(653, 1, '2014-11-18 15:29:31', '2014-11-18 14:29:31', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:29:31', '2014-11-18 14:29:31', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(654, 1, '2014-11-18 15:32:37', '2014-11-18 14:32:37', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:32:37', '2014-11-18 14:32:37', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(655, 1, '2014-11-18 15:36:57', '2014-11-18 14:36:57', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:36:57', '2014-11-18 14:36:57', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(656, 1, '2014-11-18 15:38:06', '2014-11-18 14:38:06', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:38:06', '2014-11-18 14:38:06', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(657, 1, '2014-11-18 15:39:23', '2014-11-18 14:39:23', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:39:23', '2014-11-18 14:39:23', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(658, 1, '2014-11-18 15:45:55', '2014-11-18 14:45:55', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-18 15:45:55', '2014-11-18 14:45:55', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(659, 1, '2014-11-25 10:46:35', '2014-11-25 09:46:35', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-11-25 10:46:35', '2014-11-25 09:46:35', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(661, 1, '2014-12-04 18:35:19', '2014-12-04 17:35:19', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-12-04 18:35:19', '2014-12-04 17:35:19', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(662, 1, '2014-12-10 18:47:12', '2014-12-10 17:47:12', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2014-12-10 18:47:12', '2014-12-10 17:47:12', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(663, 1, '2014-12-10 18:47:16', '2014-12-10 17:47:16', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Auzias G, Colliot O, Glaunès JA, Perrot M, Mangin JF, Trouvé A, Baillet S. Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Transactions on Medical Imaging. 30(6):1214-27, 2011. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Fillard P, Pennec X, Trouvé A, Ayache N. Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents, NeuroImage. 55(3):1073-1090, 2011. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Thompson P, Ayache N. Inferring Brain Variability from Diffeomorphic Deformations of Currents: an integrative approach, Medical Image Analysis. 12(5):626-637, 2008. Paper in PDF
Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117(6):1300-10, 2012. Paper in PDF
Henry TR, Chupin M, Lehéricy S, Strupp JP, Sikora MA, Sha ZY, Ugurbil K, Van de Moortele PF. Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T. Radiology. 261(1):199-209, 2011.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S, Samson Y, Dormont D. Diffusion-weighted MR imaging in acute stroke within the first 6 hours : 1.5 or 3.0 Tesla? Neurology. 74:1946-53, 2010.
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Thivard L, Bouilleret V, Chassoux F, Adam C, Dormont D, Baulac M, Semah F, Dupont S. Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive. EpilepsyRes. 97(1-2):170-82, 2011.
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2015-02-08 09:55:09', '2015-02-08 08:55:09', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(673, 1, '2015-02-08 17:12:40', '2015-02-08 16:12:40', '
Maxime Corduant - UPMC - maxime.corduant@ensiie.fr
Kevin Roussel - UPMC - kevin.roussel@upmc.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2015-04-07 13:54:41', '2015-04-07 12:54:41', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(684, 1, '2015-05-27 09:35:33', '2015-05-27 08:35:33', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link : Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-05-27 09:45:36', '2015-05-27 08:45:36', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(686, 1, '2015-05-27 10:01:04', '2015-05-27 09:01:04', '
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link : Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-05-27 10:01:04', '2015-05-27 09:01:04', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(688, 1, '2015-05-27 11:43:06', '2015-05-27 10:43:06', '
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors : Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link : Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-05-27 11:43:06', '2015-05-27 10:43:06', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(689, 1, '2015-06-03 08:04:59', '2015-06-03 07:04:59', '
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-06-08 13:19:50', '2015-06-08 12:19:50', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(692, 1, '2015-06-08 13:21:17', '2015-06-08 12:21:17', '
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-06-08 13:21:17', '2015-06-08 12:21:17', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(775, 1, '2015-07-17 09:54:47', '2015-07-17 08:54:47', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Description:
Poster : J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued Data
Oral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 09:54:47', '2015-07-17 08:54:47', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(776, 1, '2015-07-17 09:55:10', '2015-07-17 08:55:10', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Description:
Poster : J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued Data
Oral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 09:55:10', '2015-07-17 08:55:10', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(777, 1, '2015-07-17 10:00:12', '2015-07-17 09:00:12', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. link
Poster : J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued Data
Oral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:00:12', '2015-07-17 09:00:12', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(778, 1, '2015-07-17 10:07:55', '2015-07-17 09:07:55', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: Aramis team participated with two works chosen respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste! Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - The Organization for Human Brain Mapping (OHBM) is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain.
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. link
Posters :
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:07:55', '2015-07-17 09:07:55', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(779, 1, '2015-07-17 10:08:44', '2015-07-17 09:08:44', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: Aramis team participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - The Organization for Human Brain Mapping (OHBM) is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain.
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. link
Posters :
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:08:44', '2015-07-17 09:08:44', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(780, 1, '2015-07-17 10:10:57', '2015-07-17 09:10:57', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: Aramis team participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: Aramis team participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:10:57', '2015-07-17 09:10:57', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(781, 1, '2015-07-17 10:12:02', '2015-07-17 09:12:02', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”.
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:12:02', '2015-07-17 09:12:02', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(782, 1, '2015-07-17 10:14:08', '2015-07-17 09:14:08', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”.
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speakers : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speakers : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speakers : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speakers : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speakers : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speakers : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speakers : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speakers : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:14:08', '2015-07-17 09:14:08', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(783, 1, '2015-07-17 10:17:15', '2015-07-17 09:17:15', '
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”.
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-07-17 10:17:15', '2015-07-17 09:17:15', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(784, 1, '2015-07-17 10:18:50', '2015-07-17 09:18:50', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-08-12 10:58:35', '2015-08-12 09:58:35', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(790, 1, '2015-09-02 16:07:56', '2015-09-02 15:07:56', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris and inspired by the research conducted by the Inria ARAMIS team.
Link: Video on YouTube
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 08:49:54', '2015-10-09 07:49:54', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(798, 1, '2015-10-09 08:56:44', '2015-10-09 07:56:44', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
Link: Video on YouTube
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 08:56:44', '2015-10-09 07:56:44', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(799, 1, '2015-10-09 08:57:20', '2015-10-09 07:57:20', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations to Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
Link: Video on YouTube
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 08:57:20', '2015-10-09 07:57:20', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(800, 1, '2015-10-09 08:58:04', '2015-10-09 07:58:04', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
Link: Video on YouTube
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 08:58:04', '2015-10-09 07:58:04', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(801, 1, '2015-10-09 09:02:04', '2015-10-09 08:02:04', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 09:02:04', '2015-10-09 08:02:04', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(802, 1, '2015-10-09 09:04:26', '2015-10-09 08:04:26', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Very well done!
Link: Video
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 09:04:26', '2015-10-09 08:04:26', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(803, 1, '2015-10-09 09:05:31', '2015-10-09 08:05:31', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Well done!
Link: Video
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 09:05:31', '2015-10-09 08:05:31', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(804, 1, '2015-10-09 10:17:00', '2015-10-09 09:17:00', '
TV Report - French TV newsmagazine Zone Interdite (M6)
Date : 27th September 2015
Location : ICM, Brain and Spine Institute, Paris
Authors: Mario Chavez, Fabrizio De Vico Fallani, Fanny Grosselin, Laurent Hugueville, Xavier Navarro
Description: Report realized by the team of Wendy Bouchard of the French TV newsmagazine "Zone Interdite" broadcast on M6 once every two weeks on Sunday prime time. This report has been performed at the EEG/MEG Centre of the Brain and Spine Institute (ICM) in Paris. It was about the research conducted by the Aramis team on the Brain-computer interface. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier. Well done!
Link: MakingOf , Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-09 10:17:00', '2015-10-09 09:17:00', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(806, 3, '2017-11-23 22:08:17', '2017-11-23 21:08:17', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-autosave-v1', '', '', '2017-11-23 22:08:17', '2017-11-23 21:08:17', '', 30, 'https://www.aramislab.fr/30-autosave-v1/', 0, 'revision', '',
(807, 3, '2015-10-23 14:14:00', '2015-10-23 13:14:00', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-10-23 14:24:33', '2015-10-23 13:24:33', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(810, 1, '2015-10-25 11:26:46', '2015-10-25 10:26:46', '
Xavier Badé - Clinical Research Associate - xavier.bade@upmc.fr
Yohan Attal - Postdoctoral fellow - webpage - yohan.attal@upmc.fr, now CEO of MyBrainTechnologies
Maxime Corduant - Master\'s student - maxime.corduant@ensiie.fr
Kevin Roussel - Master\'s student - kevin.roussel@upmc.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2015-10-25 11:30:51', '2015-10-25 10:30:51', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(814, 3, '2015-11-13 17:36:00', '2015-11-13 16:36:00', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2015-11-13 17:36:00', '2015-11-13 16:36:00', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(815, 3, '2015-11-13 17:36:56', '2015-11-13 16:36:56', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2015-11-13 17:43:50', '2015-11-13 16:43:50', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(818, 3, '2016-01-11 16:54:25', '2016-01-11 15:54:25', '
Jan 8: Congratulations to Pietro Gori!
who successfully defended his PhD today about "Statistical models to learn the structural organisation of neural circuits from multimodal brain images, with application to Gilles de la Tourette syndrome".
Dec 21: Congratulations to Takoua Kaaouana!
who successfully defended her PhD today on the "detection and characterization of brain micro-bleeds with applications on mutli-centric clinical data".
Nov 13: New postdoc position opened!
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2016-01-11 16:54:25', '2016-01-11 15:54:25', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(819, 3, '2016-01-11 16:57:31', '2016-01-11 15:57:31', '
Jan 8: Congratulations to Pietro Gori!
who successfully defended his PhD today about "Statistical models to learn the structural organisation of neural circuits from multimodal brain images, with application to Gilles de la Tourette syndrome".
Dec 21: Congratulations to Takoua Kaaouana!
who successfully defended her PhD today on the "detection and characterization of brain micro-bleeds with applications on mutli-centric clinical data".
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2016-01-11 16:57:31', '2016-01-11 15:57:31', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(821, 1, '2016-01-13 13:52:38', '2016-01-13 12:52:38', '
Xavier Badé - Clinical Research Associate - xavier.bade@upmc.fr
Yohan Attal - Postdoctoral fellow - webpage - yohan.attal@upmc.fr, now CEO of MyBrainTechnologies
Maxime Corduant - Master\'s student - maxime.corduant@ensiie.fr
Kevin Roussel - Master\'s student - kevin.roussel@upmc.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2016-01-13 13:52:38', '2016-01-13 12:52:38', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(823, 1, '2016-01-20 10:16:43', '2016-01-20 09:16:43', '', 'stage_segmentation_hippoHR_finale20160119', '', 'inherit', 'closed', 'closed', '', 'stage_segmentation_hippohr_finale20160119', '', '', '2016-01-20 10:16:43', '2016-01-20 09:16:43', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/01/stage_segmentation_hippoHR_finale20160119.pdf', 0, 'attachment', 'application/pdf',
(824, 1, '2016-01-20 10:21:27', '2016-01-20 09:21:27', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-01-20 10:21:27', '2016-01-20 09:21:27', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(825, 1, '2016-01-20 10:23:12', '2016-01-20 09:23:12', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Xavier Badé - Clinical Research Associate - xavier.bade@upmc.fr
Yohan Attal - Postdoctoral fellow - webpage - yohan.attal@upmc.fr, now CEO of MyBrainTechnologies
Maxime Corduant - Master\'s student - maxime.corduant@ensiie.fr
Kevin Roussel - Master\'s student - kevin.roussel@upmc.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2016-01-26 17:53:01', '2016-01-26 16:53:01', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(829, 1, '2016-02-01 10:39:18', '2016-02-01 09:39:18', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-02-01 10:39:18', '2016-02-01 09:39:18', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(830, 1, '2016-02-01 10:39:36', '2016-02-01 09:39:36', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-02-01 10:39:36', '2016-02-01 09:39:36', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(832, 1, '2016-02-01 10:40:25', '2016-02-01 09:40:25', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-02-01 10:40:25', '2016-02-01 09:40:25', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(833, 1, '2016-02-01 10:41:13', '2016-02-01 09:41:13', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
INRIA official website features the research of the ARAMIS Lab on the occasion of the French "Brain week". Full article here (in French), and more pics there. Enjoy and Share!
Jan 8: Congratulations to Pietro Gori!
who successfully defended his PhD today about "Statistical models to learn the structural organisation of neural circuits from multimodal brain images, with application to Gilles de la Tourette syndrome".
Dec 21: Congratulations to Takoua Kaaouana!
who successfully defended her PhD today on the "detection and characterization of brain micro-bleeds with applications on mutli-centric clinical data".
The ARAMIS lab recruits high-profile researchers, data scientists and engineers with a solid background in statistical learning, data science and/or medical imaging. More info here or in the job offer section!
Nov 13: New position opened for a software developer!
We recruit a talented software developer to develop a product for the construction of virtual models of brain disease progression and its validation in a clinical environment. More info here or in the job offer section!
Oct 23: New PhD position opened!
We are looking for highly motivated students with a strong background in statistical learning and data analysis. The PhD will be about the development of novel learning methods for the statistical exploitation of multimodal longitudinal data sets. More info here or in the job offer section!
Sept 27: ARAMIS in the news!
Aramis was broadcasted yesterday in the French TV newsmagazine Zone Interdite (M6) on Sunday prime time. The report was about the brain-computer interface set up at our EEG/MEG Centre. The journalist managed to write the word "Fascinant!" using only her brain. Congratulations Mario, Fabrizio, Fanny, Laurent and Xavier! Well done!
See the MakingOf or the Report
Conference IPMI 2015 - Information Processing in Medical Imaging
Date : 28th June - 3rd July 2015
Location : Sabhal Mor Ostaig College, Isle of Skye, Scotland
Description: One of the preeminent international forums for presentation of leading-edge research in the medical imaging field. linkComments: The Aramis Lab participated with two works selected respectively for oral and poster presentation. The poster was then chosen by the participants for an oral presentation. Bravo Jean-Baptiste!
Poster: J.-B. Schiratti, S. Allassonnière, O. Colliot and S. Durrleman - A Mixed Effect Model with Time Reparametrization for Longitudinal Univariate Manifold-valued DataOral : P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, S. Lecomte, C. Poupon, A. Hartmann, N. Ayache and S. Durrleman - Joint Morphometry of Fiber Tracts and Gray Matter structures using Double Diffeomorphisms
Conference OHBM 2015 - Organization for Human Brain Mapping
Date : June 14th - 18th 2015
Location : Honolulu, Hawaii, USA
Description: It is the primary international organization dedicated to using neuroimaging to discover the organization of the human brain. linkComments: The Aramis Lab has massively participated with 14 posters and a seminary talk of our team leader Olivier during the full-day course “Pattern Recognition for NeuroImaging”. More...
Research Highlights - IEEE Engineering in Medicine and Biology
Authors: Fabrizio De Vico Fallani et al.
Title: Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of In Vivo Calcium Imaging Data
Journal: IEEE Transaction Neural Systems and Rehabilitation Engineering, 23, 2015
Link: Paper
Seminar - Denis Le Bihan - May 18th 2015
Date : May 18th, 2015 at 11:00am
Speaker : Denis Le Bihan (Neurospin, CEA)
Title: Diffusion MRI: What water tells us about the brain
Seminar - Ron Kikinis - May 11th 2015
Date : May 11th, 2015 at 11:00am
Speaker : Ron Kikinis (Harvard Medical School)
Title: Medical Image Computing
Seminar - Daniel Rueckert - March 23rd 2015
Date : March 23rd, 2015 at 11:00am
Speaker : Daniel Rueckert (Imperial College London)
Title: Learning clinically useful information from brain images
Thesis Defense - Claire Cury - Febrary 12th 2015
Date : February 12th, 2015 at 14:30
Phd student : Claire Cury
Title: Analyse statistique de la variabilité anatomique de l\'hippocampe à partir de grandes populations.
Seminar - Nicholas Ayache - February 9th 2015
Date : February 9th, 2015 at 11:00am
Speaker : Nicholas Ayache (Inria, Sophia-Antipolis)
Title: Medical Image Computing: towards a personalized Digital Patient
Seminar - Marc Modat - January 29th 2015
Date : January 29th, 2015 at 10:30am
Speaker : Marc Modat (University College London)
Title: Medical image analysis approaches for imaging biomarker development
Seminar - Jean-François Mangin - October 20th 2014
Date : October 20th, 2014 at 11:00am
Speaker : Jean-François Mangin (CEA)
Title: Turning shapes into biomarkers
Seminar - Sophie Achard - October 9th 2014
Date : October 9th, 2014 at 11:30am
Speaker : Sophie Achard (GIPSA Labs, Grenoble)
Title: Hubs of brain functional networks are radically reorganized in comatose patients
Outstanding Paper - Fabrizio de Vico Fallani, Mario Chavez et al.
Authors: Fabrizio De Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard
Title: Graph analysis of functional brain networks: practical issues in translational neuroscience
Journal: Philosophical Transactions of the Royal Society B, 369, 2014
Link: Paper
Seminar - Andrew Gelman - June 26th 2014
Date : June 26st, 2014 at 11am
Speaker : Andrew Gelman (Columbia University)
Title: Of beauty, sex, and power: Statistical challenges in estimating small effects
Date : May 27st, 2014 at 11am
Speakers : Rodolphe Thiébaut & Carole Dufouil (ISPED - Université de Bordeaux / Inserm / INRIA)
Title: Systems approaches in epidemiology: from vaccinology to imaging of cerebral aging
Seminar - Pierre-Louis Bazin - April 22th 2014
Date : April 22st, 2014 at 11am
Speaker : Pierre-Louis Bazin (Max Planck Institute, Leipzig)
Title: In-vivo analysis of brain anatomy at 7 Tesla
Seminar - François Rousseau - March 27th 2014
Date : March 27st, 2014 at 11am
Speaker : François Rousseau (CNRS/ Strasbourg University)
Title: Fetal brain development study using MRI
Seminar - Petra Vertes - February 27th 2014
Date : February 27th at 11am
Speaker : Petra Vertes (Cambridge University)
Title: Modelling macroscopic and microscopic brain networks
Seminar - Daniele Marinazzo - January 30th 2014
Date : January 30th at 2pm
Speaker : Daniele Marinazzo (University of Gent)
Title: Dynamical networks in the brain: insights from information theory and criticality
Seminar - John Ashburner - November 28th 2013
Date : November 28st, 2013 at 11am
Speaker : John Ashburner (University College London)
Title: Growth and Atrophy Modelling With Diffeomorphisms
', 'News', '', 'inherit', 'closed', 'closed', '', '521-revision-v1', '', '', '2016-03-15 11:27:32', '2016-03-15 10:27:32', '', 521, 'https://www.aramislab.fr/521-revision-v1/', 0, 'revision', '',
(841, 1, '2016-03-21 10:02:58', '2016-03-21 09:02:58', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett. 104:118701, 2010. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (3), 682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E. 89, 012802, 2013. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data, International Journal of Computer Vision. 103(1):22-59, 2013. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett. 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 11:11:50', '2016-04-11 10:11:50', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(847, 1, '2016-04-11 11:38:29', '2016-04-11 10:38:29', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013.
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014.
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014.
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015.
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015.
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015.
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 11:38:29', '2016-04-11 10:38:29', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(848, 1, '2016-04-11 12:49:32', '2016-04-11 11:49:32', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014.
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013.
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014.
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015.
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014.
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015.
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015.
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015.
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015.
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:49:32', '2016-04-11 11:49:32', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(849, 1, '2016-04-11 12:50:32', '2016-04-11 11:50:32', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:50:32', '2016-04-11 11:50:32', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(850, 1, '2016-04-11 12:51:41', '2016-04-11 11:51:41', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:51:41', '2016-04-11 11:51:41', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(851, 1, '2016-04-11 12:53:29', '2016-04-11 11:53:29', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:53:29', '2016-04-11 11:53:29', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(852, 1, '2016-04-11 12:54:21', '2016-04-11 11:54:21', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:54:21', '2016-04-11 11:54:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(853, 1, '2016-04-11 12:55:12', '2016-04-11 11:55:12', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:55:12', '2016-04-11 11:55:12', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(854, 1, '2016-04-11 12:59:36', '2016-04-11 11:59:36', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 12:59:36', '2016-04-11 11:59:36', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(855, 1, '2016-04-11 13:03:13', '2016-04-11 12:03:13', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Phys Rev Lett 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Phys Rev E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys Rev Lett 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 13:03:13', '2016-04-11 12:03:13', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(856, 1, '2016-04-11 13:05:25', '2016-04-11 12:05:25', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, Fallani FDV, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 13:05:25', '2016-04-11 12:05:25', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(857, 1, '2016-04-11 13:19:41', '2016-04-11 12:19:41', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, De Vico Fallani F, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 13:19:41', '2016-04-11 12:19:41', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(858, 1, '2016-04-11 13:21:32', '2016-04-11 12:21:32', '', 'PROOF_neuroinformatics', '', 'inherit', 'closed', 'closed', '', 'proof_neuroinformatics', '', '', '2016-04-11 13:21:32', '2016-04-11 12:21:32', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/04/PROOF_neuroinformatics.pdf', 0, 'attachment', 'application/pdf',
(859, 1, '2016-04-11 13:22:22', '2016-04-11 12:22:22', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, De Vico Fallani F, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 13:22:22', '2016-04-11 12:22:22', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(860, 1, '2016-04-11 13:27:53', '2016-04-11 12:27:53', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, De Vico Fallani F, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging, Lecture Notes in Computer Science Springer International Publishing, pp., 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp., 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 13:27:53', '2016-04-11 12:27:53', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(861, 1, '2016-04-11 21:19:22', '2016-04-11 20:19:22', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Chavez M, Valencia M, Latora V, Martinerie J. Functional modularity of background activities in normal and epileptic brain networks. Physical Review Letters 104:118701, 2010. Paper in PDF
Chavez M, De Vico Fallani F, Valencia M, Artieda J, Mattia D, Latora V, Babiloni F. Node Accessibility in Cortical Networks During Motor Tasks. Neuroinformatics 11(3):355–366, 2013. Paper in PDF
Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749-61, 2009. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Kaaouana T, de Rochefort L, Samaille T, Thiery N, Dufouil C, Delmaire C, Dormont D, Chupin M. 2D harmonic filtering of MR phase images in multicenter clinical setting: Toward a magnetic signature of cerebral microbleeds. NeuroImage 104:287–300, 2015. Paper in PDF
Nicosia V, Valencia M, Chavez M, Diaz-Guilera A, Latora V. Remote Synchronization Reveals Network Symmetries and Functional Modules. Physical Review Letters 110: 174102, 2013. Paper in PDF
Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, Colliot O, Chupin M. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 7(11):e48953, 2012. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Simas T, Chavez M, Rodriguez P, Diaz-Guilera A. An Algebraic Topological Method for Multimodal Brain Networks Comparisons. Frontiers in Psychology 6:904, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2016-04-11 21:19:22', '2016-04-11 20:19:22', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(862, 1, '2016-04-13 08:43:15', '2016-04-13 07:43:15', '
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2016-05-02 13:53:08', '2016-05-02 12:53:08', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(872, 1, '2016-05-23 17:45:00', '2016-05-23 16:45:00', '', 'data_manager_image_analyst', '', 'inherit', 'closed', 'closed', '', 'data_manager_image_analyst', '', '', '2016-05-23 17:45:00', '2016-05-23 16:45:00', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/05/data_manager_image_analyst.pdf', 0, 'attachment', 'application/pdf',
(873, 1, '2016-05-23 17:45:05', '2016-05-23 16:45:05', '', 'software_developper', '', 'inherit', 'closed', 'closed', '', 'software_developper', '', '', '2016-05-23 17:45:05', '2016-05-23 16:45:05', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/05/software_developper.pdf', 0, 'attachment', 'application/pdf',
(874, 1, '2016-05-23 17:49:03', '2016-05-23 16:49:03', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-05-23 17:49:03', '2016-05-23 16:49:03', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(885, 3, '2016-06-06 09:51:52', '2016-06-06 08:51:52', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-06-06 09:51:52', '2016-06-06 08:51:52', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(887, 1, '2016-10-13 18:13:17', '2016-10-13 17:13:17', '', 'postdoc_hiplay7', '', 'inherit', 'closed', 'closed', '', 'postdoc_hiplay7', '', '', '2016-10-13 18:13:17', '2016-10-13 17:13:17', '', 30, 'https://www.aramislab.fr/wp-content/uploads/2014/02/postdoc_HIPLAY7.pdf', 0, 'attachment', 'application/pdf',
(888, 1, '2016-10-13 18:13:45', '2016-10-13 17:13:45', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Registration, morphometry and analysis for high-resolution 7 Tesla MRI - Duration: from 2 to 3 years - Starting date: as soon as possible - Contact: olivier.colliot@upmc.fr
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-10-13 18:13:45', '2016-10-13 17:13:45', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(889, 1, '2016-10-13 18:14:01', '2016-10-13 17:14:01', '', 'postdoc_hiplay7', '', 'inherit', 'closed', 'closed', '', 'postdoc_hiplay7-2', '', '', '2016-10-13 18:14:01', '2016-10-13 17:14:01', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/10/postdoc_HIPLAY7.pdf', 0, 'attachment', 'application/pdf',
(890, 1, '2016-10-13 18:17:28', '2016-10-13 17:17:28', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-10-13 18:17:28', '2016-10-13 17:17:28', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(893, 1, '2016-11-05 17:03:24', '2016-11-05 16:03:24', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-11-05 17:03:24', '2016-11-05 16:03:24', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(896, 1, '2016-12-05 19:54:57', '2016-12-05 18:54:57', '', 'software_developper-1', '', 'inherit', 'closed', 'closed', '', 'software_developper-1', '', '', '2016-12-05 19:54:57', '2016-12-05 18:54:57', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/12/software_developper-1.pdf', 0, 'attachment', 'application/pdf',
(897, 1, '2016-12-05 19:56:04', '2016-12-05 18:56:04', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-12-05 19:56:04', '2016-12-05 18:56:04', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(898, 1, '2016-12-06 12:21:28', '2016-12-06 11:21:28', '', 'software_developer-2', '', 'inherit', 'closed', 'closed', '', 'software_developer-2', '', '', '2016-12-06 12:21:28', '2016-12-06 11:21:28', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2016/12/software_developer-2.pdf', 0, 'attachment', 'application/pdf',
(899, 1, '2016-12-06 12:22:20', '2016-12-06 11:22:20', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-12-06 12:22:20', '2016-12-06 11:22:20', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(900, 1, '2016-12-06 12:23:11', '2016-12-06 11:23:11', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2016-12-06 12:23:11', '2016-12-06 11:23:11', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(903, 1, '2017-02-15 11:48:29', '2017-02-15 10:48:29', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Knowledge models for analysis and interpretation of genetic data - Duration: 18 months - Starting date: November 2017 - Contact: olivier.colliot@upmc.fr
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2017-02-15 11:48:29', '2017-02-15 10:48:29', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(904, 1, '2017-02-15 11:49:13', '2017-02-15 10:49:13', '', 'Post-doc-2017_IPL_Neuromarkers_For_diffusion-2', '', 'inherit', 'closed', 'closed', '', 'post-doc-2017_ipl_neuromarkers_for_diffusion-2', '', '', '2017-02-15 11:49:13', '2017-02-15 10:49:13', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2017/02/Post-doc-2017_IPL_Neuromarkers_For_diffusion-2.pdf', 0, 'attachment', 'application/pdf',
(905, 1, '2017-02-15 11:51:01', '2017-02-15 10:51:01', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2017-02-15 11:51:01', '2017-02-15 10:51:01', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '',
(906, 1, '2017-02-15 11:51:24', '2017-02-15 10:51:24', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The general aim of the team is to build numerical models of brain diseases from multimodal patient data based on the development of specific data-driven approaches. To this end, we will develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we will develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.
The general aim of the team is to build numerical models of brain diseases from multimodal patient data based on the development of specific data-driven approaches. To this end, we will develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we will develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.
The general aim of the team is to build numerical models of brain diseases from multimodal patient data based on the development of specific data-driven approaches. To this end, we will develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we will develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data.
We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Modeling brain structure from anatomical and diffusion MRI
Computational anatomy aims at building statistical models of the structure of the human brain. This endeavor requires addressing the following methodological issues: i) the extraction of geometrical objects (anatomical structures, lesions, white matter tracks...) from anatomical and diffusion-weighted MRI; ii) the design of a coherent mathematical framework to model anatomical shapes and compare them across individuals. These approaches can then be used to perform statistical studies allowing to characterize the anatomical alterations associated to a given neurological disorder, to study their progression and their correlation with symptoms and cognitive deficits.
Within this area, our main research topics are :
automatic methods to segment anatomical structures and lesions
statistical shape models based on diffeomorphic deformations
longitudinal models to study the progression of brain alterations over time
7T MRI to study the internal structure of the hippocampus
Modeling dynamical brain networks
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the interactions between the activities of different brain areas, leading to understand how cognitive processes and pathological brain states emerge from an interplay of structure and function. Our research aims to provide a coherent theoretical framework for analyzing and modeling brain dynamics in terms of time-varying functional connectivity networks. Such approaches are applied to further our understanding of abnormal connectivity during some dynamical brain diseases (e.g. Epilepsy and Alzheimer’s disease) or during natural recovery from brain damage (e.g. Stroke recovery).
Within this area, our main research topics are:
complex networks theory to characterize connectivity patterns
causality analysis
analysis of time-varying networks
brain computer interfaces for clinical applications
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
Integrating multimodal data (neuroimaging, genomics, clinical data)
Large-scale multimodal datasets, combining neuroimaging, clinical data and different types of "omics" data (genetics, transcriptomics...) are becoming a pivotal tool for neuroscience research. These datasets are complex, high-dimensional and often heterogeneous, and thus require the development of new methodologies to be fully exploited. Our team aims at developing new statistical approaches to discover relevant patterns from such datasets and to integrate multimodal data from imaging and genomics. We also have a strong practical expertise in managing and analyzing massive multicenter datasets.
Within this area, our main research topics are:
machine learning approaches to extract biomarkers
multimodal analysis approaches for bridging imaging and genomics
methodologies for multicenter neuroimaging datasets (see CATI project below)
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. This difficulty is further increased if observations take the form of structured data like images or measurements distributed at the nodes of a mesh, and if the measurements themselves are normalized data or positive definite matrices for which usual linear operations are not defined. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from the Riemannian geometry to describe trajectories of changes for any kind of data and their variability within a group both in terms of the direction of the trajectories and the pace at which trajectories are followed. The inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
CATI project: managing multicenter neuroimaging studies
Our team is strongly involved in the CATI project (PI: J-F Mangin). CATI is a joint project with Neurospin, LIF, CENIR and IM2A, funded by the Alzheimer Plan which aims at building a national platform for multicenter studies. CATI aims at mutualizing all the resources required to perform multi-center neuroimaging studies. CATI services cover harmonization of image acquisition, quality control, monitoring, databasing as well as data analysis. CATI is already in charge of the imaging core of more than fifteen large French multicenter studies in various disorders, including several therapeutic trials and the national cohort of the French Alzheimer plan (2300 subjects).
Within CATI, our team is particularly involved in:
standardization of MRI acquisitions
quality control of MRI scans
processing of anatomical MRI data
development of software for structural MRI analysis
research on new image analysis approaches (segmentation, morphometry, machine learning)
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
Spatio-temporal models to build trajectories of disease progression from longitudinal dataDecision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models of the previous axes, we propose to design systems to support clinical decisions. We first propose to personalize models of disease progression to predict evolution at the individual patient level. Models will be built using long-lasting observational studies, with many participants and multiple visits spanning large observation periods, resulting in a normative scenario of disease progression. The normative scenario comes with a set of parameters, whose values capture the inter-individual variations seen in the values of the measurements, the pathway of alteration propagation, and the onset and pace of measurement changes during the course of the disease. We will then personalize the model parameters based on individual patient data (imaging, cognitive, clinical, demographic) resulting in a personalized scenario. The personalized scenario will be able to predict which brain region will be most altered next and which symptoms will occur and when. We then propose to identify genetic factors that influence disease evolution based on sequencing or microarray data. This task is challenging due to the high-dimensionality of the data and the generally weak individual effects of genetic variants. We propose to address this through the definition of new structured regularization and sparsity approaches operating at the level of genes and pathways. The second avenue that we propose is to build content-based patient retrieval systems. For a given patient, the system will allow to retrieve similar clinical cases from a database of previously examined patients. To that purpose, we will define new ways to measure the similarity between patients, based on the previously introduced data representations. The system will then be able to provide clinical information about the retrieved case along with statistics, to inform the clinical decision. Such approaches have the potential to provide more interpretable information and thus to be more easily adopted by the clinician.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2017-02-27 21:26:15', '2017-02-27 20:26:15', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '',
(931, 1, '2017-02-27 21:36:07', '2017-02-27 20:36:07', '
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:14:59', '2017-03-01 15:14:59', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(933, 1, '2017-03-01 16:18:51', '2017-03-01 15:18:51', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface/u> (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:18:51', '2017-03-01 15:18:51', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(934, 1, '2017-03-01 16:19:21', '2017-03-01 15:19:21', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:19:21', '2017-03-01 15:19:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(935, 1, '2017-03-01 16:22:10', '2017-03-01 15:22:10', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:22:10', '2017-03-01 15:22:10', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(936, 1, '2017-03-01 16:25:05', '2017-03-01 15:25:05', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:25:05', '2017-03-01 15:25:05', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(937, 1, '2017-03-01 16:33:10', '2017-03-01 15:33:10', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, and Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:33:10', '2017-03-01 15:33:10', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(938, 1, '2017-03-01 16:34:59', '2017-03-01 15:34:59', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:34:59', '2017-03-01 15:34:59', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(939, 1, '2017-03-01 16:35:47', '2017-03-01 15:35:47', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, and others. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:35:47', '2017-03-01 15:35:47', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(940, 1, '2017-03-01 16:37:29', '2017-03-01 15:37:29', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:37:29', '2017-03-01 15:37:29', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(941, 1, '2017-03-01 16:39:50', '2017-03-01 15:39:50', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:39:50', '2017-03-01 15:39:50', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(942, 1, '2017-03-01 16:43:25', '2017-03-01 15:43:25', '
Most representative publications
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2017-03-01 16:43:25', '2017-03-01 15:43:25', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(943, 1, '2017-03-01 16:45:01', '2017-03-01 15:45:01', '
Brain network toolbox
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
SACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4]).
Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer\'s disease. Neuroimage 34:996-1019, 2007.
Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer\'s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.
Whasa
WHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for
instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical
exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own
pace. This difficulty is further increased if observations take the form of structured data like images or measurements distributed at the
nodes of a mesh, and if the measurements themselves are normalized data or positive definite matrices for which usual linear operations are
not defined. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories
from longitudinal data sets. This framework is built on tools from the Riemannian geometry to describe trajectories of changes for any kind of
data and their variability within a group both in terms of the direction of the trajectories and the pace at which trajectories are followed.
The inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2017-03-06 00:42:13', '2017-03-05 23:42:13', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '',
(1014, 7, '2017-03-06 00:43:04', '2017-03-05 23:43:04', '
Network theoretic approaches to integrate heterogeneous brain networks
T
Spatio-temporal models to build trajectories of disease progression from longitudinal dataCollaborationsNetwork theoretic approaches to integrate heterogeneous brain networks
Spatio-temporal models to build trajectories of disease progression from longitudinal dataCollaborations
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2017-03-06 00:58:00', '2017-03-05 23:58:00', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '',
(1026, 7, '2017-03-06 00:58:20', '2017-03-05 23:58:20', '
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain netwo
rks
T
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Clinica
Deformetrica
References
S. Durrleman, S., Prastawa, M., Charon, N., Kore . Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
Brain network toolbox
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Brain network toolbox
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica::
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica::
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica::
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica::
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
Contacts: stanley.durrleman@inria.fr
Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:35:27', '2018-01-24 13:35:27', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '',
(1069, 6, '2018-03-06 11:17:44', '2018-03-06 10:17:44', '
If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Postdocs / Scientists
Network models of - Duration: from 1 to 2 years - Starting date: from September 2018 - Contact: fabrizio.devicofallani@gmail.com
Brain network models of functional recovery after stroke - Duration: from 1 to 2 years - Starting date: from September 2018 - Contact: fabrizio.devicofallani@gmail.com
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Homebis', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 16:50:34', '2018-11-16 15:50:34', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1118, 7, '2018-11-16 16:50:39', '2018-11-16 15:50:39', '
Aramis
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 16:50:39', '2018-11-16 15:50:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1122, 7, '2018-11-16 16:54:56', '2018-11-16 15:54:56', '
Brain network toolbox
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques em that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
[caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-autosave-v1', '', '', '2018-11-19 12:19:52', '2018-11-19 11:19:52', '', 1098, 'https://www.aramislab.fr/1098-autosave-v1/', 0, 'revision', '',
(1192, 7, '2018-11-16 17:55:11', '2018-11-16 16:55:11', '[su_slider source="logo_ARAMISLAB_rvb clinica_icon_flat" limit="20" link="none" target="self" width="600" height="300" responsive="yes" title="yes" centered="yes" arrows="yes" pages="yes" mousewheel="yes" autoplay="5000" speed="600" class=""]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:55:11', '2018-11-16 16:55:11', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1193, 7, '2018-11-16 17:57:01', '2018-11-16 16:57:01', '[su_slider source="media: 985,982,971,968,966"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:57:01', '2018-11-16 16:57:01', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1194, 7, '2018-11-16 17:57:56', '2018-11-16 16:57:56', '[su_slider source="media: 985,982,971,968,966" height="200"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:57:56', '2018-11-16 16:57:56', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1195, 7, '2018-11-16 18:03:33', '2018-11-16 17:03:33', '
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:07:27', '2018-11-16 17:07:27', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '',
(1201, 7, '2018-11-16 18:24:37', '2018-11-16 17:24:37', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:24:37', '2018-11-16 17:24:37', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1202, 7, '2018-11-16 18:25:40', '2018-11-16 17:25:40', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
[su_label type="info"]Statistical and machine learning[/su_label]
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:25:40', '2018-11-16 17:25:40', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1203, 7, '2018-11-16 18:26:33', '2018-11-16 17:26:33', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label]
Key methodological domains :
Statistical and machine learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal models
Main applications :
Alzheimer’s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:26:33', '2018-11-16 17:26:33', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1204, 7, '2018-11-16 18:28:20', '2018-11-16 17:28:20', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:28:20', '2018-11-16 17:28:20', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1205, 7, '2018-11-16 18:28:44', '2018-11-16 17:28:44', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:28:44', '2018-11-16 17:28:44', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1206, 7, '2018-11-16 18:29:38', '2018-11-16 17:29:38', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:29:38', '2018-11-16 17:29:38', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1207, 7, '2018-11-16 18:29:51', '2018-11-16 17:29:51', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:29:51', '2018-11-16 17:29:51', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1208, 7, '2018-11-16 18:30:39', '2018-11-16 17:30:39', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:30:39', '2018-11-16 17:30:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1209, 7, '2018-11-16 18:32:20', '2018-11-16 17:32:20', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:32:20', '2018-11-16 17:32:20', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1210, 7, '2018-11-16 18:35:05', '2018-11-16 17:35:05', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:35:05', '2018-11-16 17:35:05', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1211, 7, '2018-11-16 18:40:14', '2018-11-16 17:40:14', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:40:14', '2018-11-16 17:40:14', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1212, 7, '2018-11-16 18:40:39', '2018-11-16 17:40:39', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:40:39', '2018-11-16 17:40:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1213, 7, '2018-11-16 18:41:14', '2018-11-16 17:41:14', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:41:14', '2018-11-16 17:41:14', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1214, 7, '2018-11-16 18:42:22', '2018-11-16 17:42:22', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:42:22', '2018-11-16 17:42:22', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1215, 7, '2018-11-16 18:43:36', '2018-11-16 17:43:36', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:43:36', '2018-11-16 17:43:36', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1216, 7, '2018-11-16 18:44:31', '2018-11-16 17:44:31', '', 'equipe', '', 'inherit', 'closed', 'closed', '', 'equipe', '', '', '2018-11-16 18:44:31', '2018-11-16 17:44:31', '', 1098, 'https://www.aramislab.fr/wp-content/uploads/2018/11/equipe.png', 0, 'attachment', 'image/png',
(1217, 7, '2018-11-16 18:44:54', '2018-11-16 17:44:54', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:44:54', '2018-11-16 17:44:54', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1218, 7, '2018-11-16 18:45:18', '2018-11-16 17:45:18', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:45:18', '2018-11-16 17:45:18', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1219, 7, '2018-11-16 18:48:35', '2018-11-16 17:48:35', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:48:35', '2018-11-16 17:48:35', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1220, 7, '2018-11-16 18:54:08', '2018-11-16 17:54:08', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:54:08', '2018-11-16 17:54:08', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1221, 7, '2018-11-16 18:57:15', '2018-11-16 17:57:15', '
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button]
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 20:05:47', '2018-11-16 19:05:47', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1223, 7, '2018-11-16 20:08:08', '2018-11-16 19:08:08', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
Key methododological domains
Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis
Main applications
Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 11:50:31', '2018-11-18 10:50:31', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '',
(1254, 7, '2018-11-18 11:51:30', '2018-11-18 10:51:30', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
[su_quote cite="John Doe" url="#"]As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.[/su_quote]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-autosave-v1', '', '', '2018-12-05 17:43:21', '2018-12-05 16:43:21', '', 26, 'https://www.aramislab.fr/26-autosave-v1/', 0, 'revision', '',
(1286, 7, '2018-11-19 00:07:26', '2018-11-18 23:07:26', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
·
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:07:26', '2018-11-18 23:07:26', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1287, 7, '2018-11-19 00:08:03', '2018-11-18 23:08:03', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:03', '2018-11-18 23:08:03', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1288, 7, '2018-11-19 00:08:37', '2018-11-18 23:08:37', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:37', '2018-11-18 23:08:37', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1289, 7, '2018-11-19 00:08:56', '2018-11-18 23:08:56', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:56', '2018-11-18 23:08:56', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1290, 7, '2018-11-19 00:09:06', '2018-11-18 23:09:06', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:06', '2018-11-18 23:09:06', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1291, 7, '2018-11-19 00:09:25', '2018-11-18 23:09:25', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:25', '2018-11-18 23:09:25', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1292, 7, '2018-11-19 00:09:42', '2018-11-18 23:09:42', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:42', '2018-11-18 23:09:42', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1293, 7, '2018-11-19 00:17:35', '2018-11-18 23:17:35', '
Context and general aim
Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Clinica: an open source software platform for reproducible clinical neuroscience studies
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:09', '2018-11-19 09:02:09', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1300, 7, '2018-11-19 10:02:20', '2018-11-19 09:02:20', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studies
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:20', '2018-11-19 09:02:20', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1301, 7, '2018-11-19 10:02:30', '2018-11-19 09:02:30', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:30', '2018-11-19 09:02:30', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1302, 7, '2018-11-19 10:09:54', '2018-11-19 09:09:54', '
Context and general aim
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Learning spatiotemporal trajectories from manifold-valued longitudinal data.
Schiratti et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Learning spatiotemporal trajectories from manifold-valued longitudinal data.
Schiratti et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button][su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Website[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
Contacts: olivier.colliot@inria.fr
Some papers using Clinica:
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Vito Latora[/su_tab]
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Description to complete
Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Description to complete
Vito Latora[/su_tab]
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Description to complete
Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Description to complete
Contacts: Vito Latora[/su_tab]
[/su_tabs]
[su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""]
Center for Magnetic Resonance Research, University of Minnesota, USA
Description to complete
Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil
[/su_tabs]
[su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""]
Inria Asclepios
Desription to complete
Contacts: Nicholas Ayache
[/su_tabs]
[su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""]
ENS de Cachan
Description to complete
Contact: Alain Trouvé
[/su_tabs]
[su_tab title="" disabled="no" anchor="" url="" target="blank" class=""]
Where
Description to complete
Who
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Description to complete
Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Description to complete
Contacts: Vito Latora[/su_tab][/su_tabs]
[su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""]
Center for Magnetic Resonance Research, University of Minnesota, USA
Description to complete
Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tabs]
[su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""]
Inria Asclepios
Desription to complete
Contacts: Nicholas Ayache[/su_tabs]
[su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""]
ENS de Cachan
Description to complete
Contact: Alain Trouvé
[/su_tabs]
[su_tab title="" disabled="no" anchor="" url="" target="blank" class=""]
Where
Description to complete
Who
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Description to complete
Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Description to complete
Contacts: Vito Latora[/su_tab]
[su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""]
Center for Magnetic Resonance Research, University of Minnesota, USA
Description to complete
Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tabs]
[su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""]
Inria Asclepios
Desription to complete
Contacts: Nicholas Ayache[/su_tabs]
[su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""]
ENS de Cachan
Description to complete
Contact: Alain Trouvé
[/su_tabs]
[su_tab title="" disabled="no" anchor="" url="" target="blank" class=""]
Where
Description to complete
Who
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_tabs vertical="yes"]
[su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Center for Medical Image Computing
Description to complete
Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab]
[su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""]
Scientific Computing and Imaging (SCI) Institute, University of Utah, USA
Description to complete
Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab]
[su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""]
Departement of Physics. Queen MaryUniversity of London, UK
Description to complete
Contacts: Vito Latora[/su_tab]
[su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""]
Center for Magnetic Resonance Research, University of Minnesota, USA
Description to complete
Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab]
[su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""]
Inria Asclepios
Desription to complete
Contacts: Nicholas Ayache[/su_tab]
[su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""]
ENS de Cachan
Description to complete
Contact: Alain Trouvé
[/su_tab]
[su_tab title="" disabled="no" anchor="" url="" target="blank" class=""]
Where
Description to complete
Who
[/su_tab]
[/su_tabs]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button]
[su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:56:46', '2018-11-19 10:56:46', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1383, 7, '2018-11-19 11:57:11', '2018-11-19 10:57:11', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:57:11', '2018-11-19 10:57:11', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1384, 7, '2018-11-19 11:57:46', '2018-11-19 10:57:46', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Clinica: an open source software platform for reproducible clinical neuroscience studies" style="fancy" icon="plus-circle"]Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.[/su_spoiler]
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:57:46', '2018-11-19 10:57:46', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1385, 7, '2018-11-19 11:58:40', '2018-11-19 10:58:40', '
Most representative publications
Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Clinica: an open source software platform for reproducible clinical neuroscience studies" style="fancy" icon="plus-circle"]
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
In: Lorem Ipsum
2018[/su_spoiler]
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:58:40', '2018-11-19 10:58:40', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1386, 7, '2018-11-19 12:01:01', '2018-11-19 11:01:01', '
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vido Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
As a joint research team (CNRS, Inria, Inserm, UPMC), AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:08:43', '2018-11-20 11:08:43', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1415, 7, '2018-11-20 12:10:32', '2018-11-20 11:10:32', '
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:10:32', '2018-11-20 11:10:32', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1416, 7, '2018-11-20 12:11:05', '2018-11-20 11:11:05', '
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:05', '2018-11-20 11:11:05', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1417, 7, '2018-11-20 12:11:30', '2018-11-20 11:11:30', '
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:30', '2018-11-20 11:11:30', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1418, 7, '2018-11-20 12:11:48', '2018-11-20 11:11:48', '
Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. Paper in PDF
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. Paper in PDF
Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014 Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:48', '2018-11-20 11:11:48', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1419, 7, '2018-11-20 12:12:49', '2018-11-20 11:12:49', '
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
p>References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
p>References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
Overall, Clinica helps to:
apply advanced analysis tools to clinical research studies
easily share data and results
make research more reproducible.
References
T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Reference
Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
References
Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
References
Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button]
[su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button]
[su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
References
Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014Paper
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
References
Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
Key methododological domains
[su_accordion]
[su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler]
[su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
Main applications
[su_accordion]
[su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler]
[su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler]
[/su_accordion]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References
Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-21 18:13:41', '2018-11-21 17:13:41', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1474, 1, '2018-11-21 18:15:53', '2018-11-21 17:15:53', '
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:56:02', '2018-11-22 09:56:02', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1484, 7, '2018-11-22 10:56:58', '2018-11-22 09:56:58', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:56:58', '2018-11-22 09:56:58', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1485, 7, '2018-11-22 10:57:13', '2018-11-22 09:57:13', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:57:13', '2018-11-22 09:57:13', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1486, 7, '2018-11-22 10:57:36', '2018-11-22 09:57:36', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:57:36', '2018-11-22 09:57:36', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1487, 7, '2018-11-22 17:30:32', '2018-11-22 16:30:32', '
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:36:21', '2018-11-22 16:36:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1490, 7, '2018-11-22 17:36:51', '2018-11-22 16:36:51', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:36:51', '2018-11-22 16:36:51', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1491, 7, '2018-11-22 17:46:01', '2018-11-22 16:46:01', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Bertrand et al, JAMA Neurol, 2018
Dubois et al, Lancet Neurol, 2018
Wen et al, JNNP, 2018
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:46:01', '2018-11-22 16:46:01', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1492, 7, '2018-11-22 17:51:45', '2018-11-22 16:51:45', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:51:45', '2018-11-22 16:51:45', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1493, 7, '2018-11-22 17:57:09', '2018-11-22 16:57:09', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:57:09', '2018-11-22 16:57:09', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1494, 7, '2018-11-22 18:01:17', '2018-11-22 17:01:17', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper Gonzalez. In Neuroimage ..., 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:01:17', '2018-11-22 17:01:17', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1495, 7, '2018-11-22 18:09:17', '2018-11-22 17:09:17', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper Gonzalez. In Neuroimage ..., 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois et al. . In Lancet in Neurology. ..., 2018. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:09:17', '2018-11-22 17:09:17', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1496, 7, '2018-11-22 18:09:34', '2018-11-22 17:09:34', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper Gonzalez. In Neuroimage ..., 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois et al. . In Lancet in Neurology. ..., 2018. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:09:34', '2018-11-22 17:09:34', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1497, 7, '2018-11-22 18:14:49', '2018-11-22 17:14:49', '
Clinica
References
Routier A, Habert MO, Bertrand A, Kas A, David PM, Bertin H, Godefroy O, Etcharry-Bouyx F, Moreaud O, Pasquier F, Couratier P, Bennys K, Coutoleau Bretoniere C, Martinaud O, Laurent B, Pariente J Puel M, Belliard S, Migliaccio R, Dubois B, Colliot O, Teichmann M. Structural, microstructural and metabolic alterations in Primary Progressive Aphasia variants. In OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
References
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew
ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Network theoretic approaches to integrate heterogeneous brain networks
The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper Gonzalez. In Neuroimage ..., 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois et al. . In Lancet in Neurology. ..., 2018. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:10:21', '2018-12-05 16:10:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1569, 7, '2018-12-05 17:13:24', '2018-12-05 16:13:24', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois et al. . In Lancet in Neurology. ..., 2018. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:13:24', '2018-12-05 16:13:24', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1570, 7, '2018-12-05 17:14:39', '2018-12-05 16:14:39', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet in Neurology. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:14:39', '2018-12-05 16:14:39', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1572, 7, '2018-12-05 17:18:41', '2018-12-05 16:18:41', '
Context and general aim
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet in Neurology. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:38:20', '2018-12-05 16:38:20', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1588, 7, '2018-12-05 17:43:23', '2018-12-05 16:43:23', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:43:23', '2018-12-05 16:43:23', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1589, 7, '2018-12-05 17:43:26', '2018-12-05 16:43:26', '
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al.Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al.Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., ... & Lu, P. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie).
They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'publish', 'closed', 'closed', '', 'seven-hipp', '', '', '2019-10-25 16:35:38', '2019-10-25 15:35:38', '', 0, 'https://www.aramislab.fr/?page_id=1635', 0, 'page', '',
(1636, 7, '2019-10-25 15:58:28', '2019-10-25 14:58:28', '## 7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 15:58:28', '2019-10-25 14:58:28', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1637, 7, '2019-10-25 16:02:35', '2019-10-25 15:02:35', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-autosave-v1', '', '', '2019-10-25 16:02:35', '2019-10-25 15:02:35', '', 1635, 'https://www.aramislab.fr/1635-autosave-v1/', 0, 'revision', '',
(1638, 7, '2019-10-25 16:01:46', '2019-10-25 15:01:46', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:01:46', '2019-10-25 15:01:46', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1639, 7, '2019-10-25 16:02:36', '2019-10-25 15:02:36', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:02:36', '2019-10-25 15:02:36', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1640, 7, '2019-10-25 16:02:47', '2019-10-25 15:02:47', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:02:47', '2019-10-25 15:02:47', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1641, 7, '2019-10-25 16:03:20', '2019-10-25 15:03:20', '', 'sevenhipp', '', 'inherit', 'closed', 'closed', '', 'sevenhipp', '', '', '2019-10-25 16:03:20', '2019-10-25 15:03:20', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2019/10/sevenhipp.png', 0, 'attachment', 'image/png',
(1642, 7, '2019-10-25 16:03:49', '2019-10-25 15:03:49', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:03:49', '2019-10-25 15:03:49', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1643, 7, '2019-10-25 16:04:14', '2019-10-25 15:04:14', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie).
They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:04:14', '2019-10-25 15:04:14', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1644, 7, '2019-10-25 16:04:45', '2019-10-25 15:04:45', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie).
They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:04:45', '2019-10-25 15:04:45', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1645, 7, '2019-10-25 16:16:16', '2019-10-25 15:16:16', '
7T MRI of the hippocampus
This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus.
It will be continuously updated with new code, stay tuned!
Slab registration
Code to register multiple slabs in order to create a single-slab high resolution volume.
Code is available here.
Manual segmentation of hippocampal subregions
Coming soon!
Hippocampal thickness measurement
Coming soon!
About
These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie).
They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the
right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses.
If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:16:16', '2019-10-25 15:16:16', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '',
(1649, 7, '2019-11-05 09:44:48', '2019-11-05 08:44:48', '', 'etiennemaheux', '', 'inherit', 'closed', 'closed', '', 'etiennemaheux', '', '', '2019-11-05 09:44:48', '2019-11-05 08:44:48', '', 1179, 'https://www.aramislab.fr/wp-content/uploads/2018/11/etiennemaheux.png', 0, 'attachment', 'image/png',
(1650, 7, '2019-11-05 11:12:31', '2019-11-05 10:12:31', '', 'thomas_nedelec', '', 'inherit', 'closed', 'closed', '', 'thomas_nedelec', '', '', '2019-11-05 11:13:32', '2019-11-05 10:13:32', '', 1179, 'https://www.aramislab.fr/wp-content/uploads/2018/11/thomas_nedelec.jpg', 0, 'attachment', 'image/jpeg',
(1657, 8, '2020-04-07 17:30:57', '2020-04-07 16:30:57', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
Research Engineer / Data Scientist / Software D - Neurodegenerative disease progression : Development of numerical models, Application to medical cohorts & Deployment of real-life tools.
Software Engineer R&D - Design and optimization of brain-computer interfaces (BCIs) for clinical applications - Starting date: as soon as possible
Software Engineer - Big Data Analysis of Medical Images - Starting date: as soon as possible
Software Development Engineer - Scientific Computing and High Performance Computing in Medical Imaging - Starting date: December 2017
Software Developer - Development of software for analysis of multimodal medical imaging data - Starting date: as soon as possible
Data Manager / Image Analyst - Modeling progression of Alzheimer’s disease from brain imaging data - Starting date: as soon as possible
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
Decision support systems for diagnosis, prognosis and design of clinical trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F,
Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. Paper in PDF
Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-autosave-v1', '', '', '2020-09-09 18:30:49', '2020-09-09 17:30:49', '', 26, 'https://www.aramislab.fr/26-autosave-v1/', 0, 'revision', '', 0);
INSERT INTO `wp_aramis_posts` VALUES (1690, 9, '2020-09-09 18:23:35', '2020-09-09 17:23:35', '
Most representative publications
Networks
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F,
Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. Paper in PDF
Wen, J, Thibeau-Sutre, E, Samper-González, J, Routier, A, Bottani, S, Durrleman, S, Burgos, N, Colliot, O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
Full list of publications
Here is a link to our publications on the open archive HAL.
', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2020-09-09 18:23:35', '2020-09-09 17:23:35', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '',
(1691, 9, '2020-09-09 18:36:59', '2020-09-09 17:36:59', '
Clinica
References
Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In OHBM 2018. Paper in PDF
Routier A, Marcoux A, Diaz Melo M, Guillon J, Samper-González J, Wen J, Bottani S, Guyot A, Thibeau-Sutre E, Teichmann M, Habert M-O, Durrleman S, Burgos N, Colliot O: New Advances in the Clinica Software Platform for Clinical Neuroimaging Studies. In OHBM 2019. Paper in PDF
Routier A, Marcoux A, Diaz Melo M, Samper-González J, Wild A, Guyot A, Wen J, Thibeau- Sutre E, Bottani S, Durrleman S, Burgos N, Colliot O: New Longitudinal and Deep Learning Pipelines in the Clinica Software Platform. In OHBM, 2020. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. Paper in PDF
Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Marcoux A, Burgos N, Bertrand A, Teichmann M, Routier A, Wen J, Samper-González J, Bottani S, Durrleman S, Habert M-O, Colliot O: An Automated Pipeline for the Analysis of PET Data on the Cortical Surface’. Frontiers in Neuroinformatics, 12, 2018. Paper in PDF
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 Paper in PDF
Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. Paper in PDF
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. Paper in PDF
De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017. Paper in PDF
De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. Paper in PDF
Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. Paper in PDF
De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. Paper in PDF
Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017. Paper in PDF
Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F,
Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. Paper in PDF
Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. Paper in PDF
Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. Paper in PDF
Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. Paper in PDF
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. Paper in PDF
Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017. Paper in PDF
Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584 Paper in PDF
Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. Paper in PDF
Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. Paper in PDF
Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. Paper in PDF
Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 Paper in PDF
Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. Paper in PDF
Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. Paper in PDF
Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. Paper in PDF
Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 Paper in PDF
Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. Paper in PDF
Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. Paper in PDF
Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. Paper in PDF
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).
The latest publications of the members of Aramis are illustrated below.
Click on any of them to discover the authors, a general overview of the article, the abstract and much more.
', 'Latest publications', '', 'inherit', 'closed', 'closed', '', '1778-autosave-v1', '', '', '2021-01-11 19:56:10', '2021-01-11 18:56:10', '', 1778, 'https://www.aramislab.fr/uncategorized/1778-autosave-v1/', 0, 'revision', '',
(1785, 8, '2021-01-11 19:55:14', '2021-01-11 18:55:14', '
Latest publications
The latest publications of the members of Aramis are illustrated below.
Click on any of them to discover the authors, a general overview of the article, the abstract and much more.', 'Latest publications', '', 'inherit', 'closed', 'closed', '', '1778-revision-v1', '', '', '2021-01-11 19:55:14', '2021-01-11 18:55:14', '', 1778, 'https://www.aramislab.fr/uncategorized/1778-revision-v1/', 0, 'revision', '',
(1786, 8, '2021-01-11 19:55:30', '2021-01-11 18:55:30', '
Latest publications
The latest publications of the members of Aramis are illustrated below.
Click on any of them to discover the authors, a general overview of the article, the abstract and much more.', 'Latest publications', '', 'inherit', 'closed', 'closed', '', '1778-revision-v1', '', '', '2021-01-11 19:55:30', '2021-01-11 18:55:30', '', 1778, 'https://www.aramislab.fr/uncategorized/1778-revision-v1/', 0, 'revision', '',
(1787, 8, '2021-01-11 19:55:51', '2021-01-11 18:55:51', '
Latest publications
The latest publications of the members of Aramis are illustrated below.
Click on any of them to discover the authors, a general overview of the article, the abstract and much more.
', 'Latest publications', '', 'inherit', 'closed', 'closed', '', '1778-revision-v1', '', '', '2021-01-11 19:55:51', '2021-01-11 18:55:51', '', 1778, 'https://www.aramislab.fr/uncategorized/1778-revision-v1/', 0, 'revision', '',
(1788, 8, '2021-01-11 19:56:11', '2021-01-11 18:56:11', '
Latest publications
The latest publications of the members of Aramis are illustrated below.
Click on any of them to discover the authors, a general overview of the article, the abstract and much more.
', 'Latest publications', '', 'inherit', 'closed', 'closed', '', '1778-revision-v1', '', '', '2021-01-11 19:56:11', '2021-01-11 18:56:11', '', 1778, 'https://www.aramislab.fr/uncategorized/1778-revision-v1/', 0, 'revision', '',
(1791, 8, '2021-01-11 20:10:55', '2021-01-11 19:10:55', '', 'Figure 1 - Boxplots', '', 'inherit', 'closed', 'closed', '', 'figure-1-boxplots', '', '', '2021-01-11 20:10:55', '2021-01-11 19:10:55', '', 1748, 'https://www.aramislab.fr/wp-content/uploads/2021/01/Figure-1-Boxplots.png', 0, 'attachment', 'image/png',
(1792, 8, '2021-01-11 20:13:29', '2021-01-11 19:13:29', 'a:7:{s:4:"type";s:3:"url";s:12:"instructions";s:108:"Link to the open access publication (if any - though we highly encourage you to have one, especially on HAL)";s:8:"required";i:0;s:17:"conditional_logic";i:0;s:7:"wrapper";a:3:{s:5:"width";s:0:"";s:5:"class";s:0:"";s:2:"id";s:0:"";}s:13:"default_value";s:0:"";s:11:"placeholder";s:0:"";}', 'Open access Link', 'open_access_link', 'publish', 'closed', 'closed', '', 'field_5ffca31d9e5f6', '', '', '2021-01-11 21:19:54', '2021-01-11 20:19:54', '', 1733, 'https://www.aramislab.fr/?post_type=acf-field&p=1792', 7, 'acf-field', '',
(1793, 8, '2021-01-11 20:59:12', '2021-01-11 19:59:12', 'a:7:{s:4:"type";s:3:"tab";s:12:"instructions";s:0:"";s:8:"required";i:0;s:17:"conditional_logic";i:0;s:7:"wrapper";a:3:{s:5:"width";s:0:"";s:5:"class";s:0:"";s:2:"id";s:0:"";}s:9:"placement";s:3:"top";s:8:"endpoint";i:0;}', 'Image', 'image', 'publish', 'closed', 'closed', '', 'field_5ffcade967de6', '', '', '2021-01-11 21:26:28', '2021-01-11 20:26:28', '', 1733, 'https://www.aramislab.fr/?post_type=acf-field&p=1793', 8, 'acf-field', '',
(1795, 8, '2021-01-11 21:23:58', '2021-01-11 20:23:58', 'a:15:{s:4:"type";s:5:"image";s:12:"instructions";s:0:"";s:8:"required";i:1;s:17:"conditional_logic";i:0;s:7:"wrapper";a:3:{s:5:"width";s:0:"";s:5:"class";s:0:"";s:2:"id";s:0:"";}s:13:"return_format";s:5:"array";s:12:"preview_size";s:6:"medium";s:7:"library";s:3:"all";s:9:"min_width";s:0:"";s:10:"min_height";s:0:"";s:8:"min_size";s:0:"";s:9:"max_width";s:0:"";s:10:"max_height";s:0:"";s:8:"max_size";s:0:"";s:10:"mime_types";s:0:"";}', 'Image', 'image', 'publish', 'closed', 'closed', '', 'field_5ffcb3a32211f', '', '', '2021-01-12 09:37:04', '2021-01-12 08:37:04', '', 1733, 'https://www.aramislab.fr/?post_type=acf-field&p=1795', 9, 'acf-field', '',
(1797, 11, '2021-01-12 08:54:55', '2021-01-12 07:54:55', '', 'Deep learning for brain disorders', '', 'publish', 'closed', 'closed', '', 'deep-learning-for-brain-disorders', '', '', '2021-01-12 10:24:47', '2021-01-12 09:24:47', '', 0, 'https://www.aramislab.fr/?post_type=publication&p=1797', 0, 'publication', '',
(1799, 11, '2021-01-12 08:54:31', '2021-01-12 07:54:31', '', 'Figure_DL_architectures', 'Common deep learning architectures for brain disorders.
a) U-Net is the most popular architecture for biomedical image segmentation. It consists of a contraction (or encoder) path, where the size of the image gradually reduces while the depth gradually increases, and an expansion (or decoder) path, where the size of the image gradually increases and the depth gradually decreases. To obtain more precise locations, skip connections are used between the encoder and decoder blocks. U-Net architectures have also been used for image reconstruction and synthesis. b) Autoencoder learns a latent representation (code) of the input data that minimizes the reconstruction error. Autoencoders have been used for disease detection, prediction of treatment and integration of multimodal data. c) Variational autoencoder is a variant of the autoencoder where the latent representation is a distribution. Variational autoencoders have been used for image segmentation, disease detection and disease subtyping. d) Generative adversarial network consists of a generator producing new samples and a discriminator classifying samples as original or generated. Generative adversarial networks can be used for data augmentation. e) Conditional generative adversarial network is a variant of the generative adversarial network where the generator and the discriminator are conditioned by another feature. Conditional generative adversarial networks have been used for signal enhancement, image synthesis and disease prediction.', 'inherit', 'closed', 'closed', '', 'figure_architectures', '', '', '2021-01-12 08:54:43', '2021-01-12 07:54:43', '', 1797, 'https://www.aramislab.fr/wp-content/uploads/2021/01/Figure_architectures.jpg', 0, 'attachment', 'image/jpeg',
(1800, 8, '2021-01-12 09:37:04', '2021-01-12 08:37:04', 'a:10:{s:4:"type";s:4:"text";s:12:"instructions";s:20:"Caption of the image";s:8:"required";i:0;s:17:"conditional_logic";i:0;s:7:"wrapper";a:3:{s:5:"width";s:0:"";s:5:"class";s:0:"";s:2:"id";s:0:"";}s:13:"default_value";s:0:"";s:11:"placeholder";s:0:"";s:7:"prepend";s:0:"";s:6:"append";s:0:"";s:9:"maxlength";s:0:"";}', 'Caption', 'caption', 'publish', 'closed', 'closed', '', 'field_5ffd5f2b99c98', '', '', '2021-01-12 09:37:04', '2021-01-12 08:37:04', '', 1733, 'https://www.aramislab.fr/?post_type=acf-field&p=1800', 10, 'acf-field', '',
(1803, 11, '2021-01-15 15:39:20', '2021-01-15 14:39:20', '', 'A Reliable and Rapid Language Tool for the Diagnosis, Classification, and Follow-Up of Primary Progressive Aphasia Variants', '', 'publish', 'closed', 'closed', '', 'diagnosis-tool-primary-progressive-aphasia', '', '', '2021-01-15 16:25:23', '2021-01-15 15:25:23', '', 0, 'https://www.aramislab.fr/?post_type=publication&p=1803', 0, 'publication', '',
(1804, 11, '2021-01-15 15:33:38', '2021-01-15 14:33:38', '', 'Image_1_A Reliable and Rapid Language Tool for the Diagnosis, Classification, and Follow-Up of Primary Progressive Aphasia Variants', '', 'inherit', 'closed', 'closed', '', 'image_1_a-reliable-and-rapid-language-tool-for-the-diagnosis-classification-and-follow-up-of-primary-progressive-aphasia-variants', '', '', '2021-01-15 15:33:38', '2021-01-15 14:33:38', '', 1803, 'https://www.aramislab.fr/wp-content/uploads/2021/01/Image_1_A-Reliable-and-Rapid-Language-Tool-for-the-Diagnosis-Classification-and-Follow-Up-of-Primary-Progressive-Aphasia-Variants.tif', 0, 'attachment', 'image/tiff',
(1805, 11, '2021-01-15 16:07:10', '2021-01-15 15:07:10', '', 'the PARIS scale', '', 'inherit', 'closed', 'closed', '', 'the-paris-scale', '', '', '2021-01-15 16:07:10', '2021-01-15 15:07:10', '', 1803, 'https://www.aramislab.fr/wp-content/uploads/2021/01/the-PARIS-scale.jpg', 0, 'attachment', 'image/jpeg',
(1807, 11, '2021-01-16 17:17:07', '2021-01-16 16:17:07', '', 'Systematically underestimating one\'s own cognitive decline may be a sign of Alzheimer\'s', '', 'publish', 'closed', 'closed', '', 'systematically-underestimating-ones-own-cognitive-decline-may-be-a-sign-of-alzheimers', '', '', '2021-01-16 17:17:07', '2021-01-16 16:17:07', '', 0, 'https://www.aramislab.fr/?post_type=publication&p=1807', 0, 'publication', '',
(1808, 11, '2021-01-16 17:14:15', '2021-01-16 16:14:15', '', 'ACDI M36', '', 'inherit', 'closed', 'closed', '', '122096318_954861568341908_4699321636902992732_o', '', '', '2021-01-16 17:14:38', '2021-01-16 16:14:38', '', 1807, 'https://www.aramislab.fr/wp-content/uploads/2021/01/122096318_954861568341908_4699321636902992732_o.jpg', 0, 'attachment', 'image/jpeg',
(1809, 1, '2021-02-02 18:06:28', '2021-02-02 17:06:28', '', 'postdoc_EN-v1', '', 'inherit', 'closed', 'closed', '', 'postdoc_en-v1', '', '', '2021-02-02 18:06:28', '2021-02-02 17:06:28', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2021/02/postdoc_EN-v1.pdf', 0, 'attachment', 'application/pdf',
(1810, 1, '2021-02-02 18:08:44', '2021-02-02 17:08:44', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).